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Wiki source code of Research at a Glance

Version 110.1 by Ryan C on 2025/06/19 02:53

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105.1	1	{{toc/}}
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2.1	4	= Research at a Glance =
	5
4.1	6
2.1	7
107.1	8	Welcome to the Research at a Glance repository. This section serves as a centralized reference hub for key academic studies related to various important Racial themes. Each study is categorized for easy navigation and presented in a collapsible format to maintain a clean layout. I wanted to make this for a couple of reasons. Number one is organization. There are a ton of useful studies out there that expose the truth, sometimes inadvertently. You'll notice that in this initial draft the summaries are often woke and reflect the bias of the AI writing them as well as the researchers politically correct conclusion in most cases. That's because I haven't gotten to going through and pointing out the reasons I put all of them in here.
4.1	9
85.1	10
86.1	11	There is often an underlying hypocrisy or double standard, saying the quiet part out loud, or conclusions that are so much of an antithesis to what the data shows that made me want to include it. At least, thats the idea for once its polished. I have about 150 more studies to upload, so it will be a few weeks before I get through it all. Until such time, feel free to search for them yourself and edit in what you find, or add your own studies. If you like you can do it manually, or if you'd rather go the route I did, just rename the study to its doi number and feed the study into an AI and tell them to summarize the study using the following format:
85.1	12
	13
83.1	14
2.1	15	- Click on a category in the Table of Contents to browse studies related to that topic.
	16	- Click on a study title to expand its details, including key findings, critique, and relevance.
	17	- Use the search function (Ctrl + F or XWiki's built-in search) to quickly find specific topics or authors.
	18	- If needed, you can export this page as PDF or print-friendly format, and all studies will automatically expand for readability.
83.1	19	- You'll also find a download link to the original full study in pdf form at the bottom of the collapsible block.
2.1	20
	21
76.1	22
77.1	23	= Genetics =
3.1	24
108.1	25	{{expandable summary="
79.1	26
107.1	27	Study: Reconstructing Indian Population History"}}
77.1	28	Source: Nature
	29	Date of Publication: 2009
	30	Author(s): David Reich, Kumarasamy Thangaraj, Nick Patterson, Alkes L. Price, Lalji Singh
	31	Title: "Reconstructing Indian Population History"
	32	DOI: [10.1038/nature08365](https://doi.org/10.1038/nature08365)
78.1	33	Subject Matter: Genetics, Population History, South Asian Ancestry
3.1	34
94.1	35	{{expandable summary="📊 Key Statistics"}}
64.1	36	1. General Observations:
77.1	37	- Study analyzed 132 individuals from 25 diverse Indian groups.
	38	- Identified two major ancestral populations: Ancestral North Indians (ANI) and Ancestral South Indians (ASI).
64.1	39
	40	2. Subgroup Analysis:
77.1	41	- ANI ancestry is closely related to Middle Easterners, Central Asians, and Europeans.
	42	- ASI ancestry is genetically distinct from ANI and East Asians.
64.1	43
	44	3. Other Significant Data Points:
77.1	45	- ANI ancestry ranges from 39% to 71% across Indian groups.
	46	- Caste and linguistic differences strongly correlate with genetic variation.
94.1	47	{{/expandable}}
64.1	48
94.1	49	{{expandable summary="🔬 Findings"}}
64.1	50	1. Primary Observations:
77.1	51	- The genetic landscape of India has been shaped by thousands of years of endogamy.
	52	- Groups with only ASI ancestry no longer exist in mainland India.
64.1	53
	54	2. Subgroup Trends:
77.1	55	- Higher ANI ancestry in upper-caste and Indo-European-speaking groups.
	56	- Andaman Islanders are unique in having ASI ancestry without ANI influence.
64.1	57
	58	3. Specific Case Analysis:
77.1	59	- Founder effects have maintained allele frequency differences among Indian groups.
	60	- Predicts higher incidence of recessive diseases due to historical genetic isolation.
94.1	61	{{/expandable}}
64.1	62
94.1	63	{{expandable summary="📝 Critique & Observations"}}
64.1	64	1. Strengths of the Study:
77.1	65	- First large-scale genetic analysis of Indian population history.
	66	- Introduces new methods for ancestry estimation without direct ancestral reference groups.
64.1	67
	68	2. Limitations of the Study:
77.1	69	- Limited sample size relative to India's population diversity.
	70	- Does not include recent admixture events post-colonial era.
64.1	71
	72	3. Suggestions for Improvement:
77.1	73	- Future research should expand sampling across more Indian tribal groups.
	74	- Use whole-genome sequencing for finer resolution of ancestry.
94.1	75	{{/expandable}}
64.1	76
94.1	77	{{expandable summary="📌 Relevance to Subproject"}}
77.1	78	- Provides a genetic basis for caste and linguistic diversity in India.
	79	- Highlights founder effects and genetic drift shaping South Asian populations.
94.1	80	- Supports research on medical genetics and disease risk prediction in Indian populations.
	81	{{/expandable}}
64.1	82
94.1	83	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	84	1. Examine genetic markers linked to disease susceptibility in Indian subpopulations.
	85	2. Investigate the impact of recent migration patterns on ANI-ASI ancestry distribution.
	86	3. Study gene flow between Indian populations and other global groups.
94.1	87	{{/expandable}}
64.1	88
94.1	89	{{expandable summary="📄 Download Full Study"}}
	90	[[Download Full Study>>attach:10.1038_nature08365.pdf]]
93.1	91	{{/expandable}}
94.1	92	{{/expandable}}
64.1	93
100.1	94	{{expandable summary="Study: The Simons Genome Diversity Project: 300 Genomes from 142 Diverse Populations"}}
	95	Source: Nature
	96	Date of Publication: 2016
	97	Author(s): David Reich, Swapan Mallick, Heng Li, Mark Lipson, and others
	98	Title: "The Simons Genome Diversity Project: 300 Genomes from 142 Diverse Populations"
	99	DOI: [10.1038/nature18964](https://doi.org/10.1038/nature18964)
	100	Subject Matter: Human Genetic Diversity, Population History, Evolutionary Genomics
64.1	101
94.1	102	{{expandable summary="📊 Key Statistics"}}
66.1	103	1. General Observations:
77.1	104	- Analyzed high-coverage genome sequences of 300 individuals from 142 populations.
	105	- Included many underrepresented and indigenous groups from Africa, Asia, Europe, and the Americas.
66.1	106
	107	2. Subgroup Analysis:
77.1	108	- Found higher genetic diversity within African populations compared to non-African groups.
	109	- Showed Neanderthal and Denisovan ancestry in non-African populations, particularly in Oceania.
66.1	110
	111	3. Other Significant Data Points:
77.1	112	- Identified 5.8 million base pairs absent from the human reference genome.
	113	- Estimated that mutations have accumulated 5% faster in non-Africans than in Africans.
94.1	114	{{/expandable}}
66.1	115
94.1	116	{{expandable summary="🔬 Findings"}}
66.1	117	1. Primary Observations:
77.1	118	- African populations harbor the greatest genetic diversity, confirming an out-of-Africa dispersal model.
	119	- Indigenous Australians and New Guineans share a common ancestral population with other non-Africans.
66.1	120
	121	2. Subgroup Trends:
77.1	122	- Lower heterozygosity in non-Africans due to founder effects from migration bottlenecks.
	123	- Denisovan ancestry in South Asians is higher than previously thought.
66.1	124
	125	3. Specific Case Analysis:
77.1	126	- Neanderthal ancestry is higher in East Asians than in Europeans.
	127	- African hunter-gatherer groups show deep population splits over 100,000 years ago.
94.1	128	{{/expandable}}
66.1	129
94.1	130	{{expandable summary="📝 Critique & Observations"}}
66.1	131	1. Strengths of the Study:
77.1	132	- Largest global genetic dataset outside of the 1000 Genomes Project.
	133	- High sequencing depth allows more accurate identification of genetic variants.
66.1	134
	135	2. Limitations of the Study:
77.1	136	- Limited sample sizes for some populations, restricting generalizability.
	137	- Lacks ancient DNA comparisons, making it difficult to reconstruct deep ancestry fully.
66.1	138
	139	3. Suggestions for Improvement:
77.1	140	- Future studies should include ancient genomes to improve demographic modeling.
	141	- Expand research into how genetic variation affects health outcomes across populations.
94.1	142	{{/expandable}}
66.1	143
94.1	144	{{expandable summary="📌 Relevance to Subproject"}}
77.1	145	- Provides comprehensive data on human genetic diversity, useful for evolutionary studies.
	146	- Supports research on Neanderthal and Denisovan introgression in modern human populations.
94.1	147	- Enhances understanding of genetic adaptation and disease susceptibility across groups.
	148	{{/expandable}}
66.1	149
94.1	150	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	151	1. Investigate functional consequences of genetic variation in underrepresented populations.
	152	2. Study how selection pressures shaped genetic diversity across different environments.
	153	3. Explore medical applications of population-specific genetic markers.
94.1	154	{{/expandable}}
66.1	155
94.1	156	{{expandable summary="📄 Download Full Study"}}
	157	[[Download Full Study>>attach:10.1038_nature18964.pdf]]
93.1	158	{{/expandable}}
94.1	159	{{/expandable}}
66.1	160
107.1	161	{{expandable summary="
66.1	162
94.1	163	Study: Meta-analysis of the heritability of human traits based on fifty years of twin studies"}}
99.1	164	Source: Nature Genetics
	165	Date of Publication: 2015
	166	Author(s): Tinca J. C. Polderman, Beben Benyamin, Christiaan A. de Leeuw, Patrick F. Sullivan, Arjen van Bochoven, Peter M. Visscher, Danielle Posthuma
	167	Title: "Meta-analysis of the heritability of human traits based on fifty years of twin studies"
	168	DOI: [10.1038/ng.328](https://doi.org/10.1038/ng.328)
	169	Subject Matter: Genetics, Heritability, Twin Studies, Behavioral Science
67.1	170
94.1	171	{{expandable summary="📊 Key Statistics"}}
67.1	172	1. General Observations:
77.1	173	- Analyzed 17,804 traits from 2,748 twin studies published between 1958 and 2012.
	174	- Included data from 14,558,903 twin pairs, making it the largest meta-analysis on human heritability.
67.1	175
	176	2. Subgroup Analysis:
77.1	177	- Found 49% average heritability across all traits.
	178	- 69% of traits follow a simple additive genetic model, meaning most variance is due to genes, not environment.
67.1	179
	180	3. Other Significant Data Points:
77.1	181	- Neurological, metabolic, and psychiatric traits showed the highest heritability estimates.
	182	- Traits related to social values and environmental interactions had lower heritability estimates.
94.1	183	{{/expandable}}
67.1	184
94.1	185	{{expandable summary="🔬 Findings"}}
67.1	186	1. Primary Observations:
77.1	187	- Across all traits, genetic factors play a significant role in individual differences.
	188	- The study contradicts models that overestimate environmental effects in behavioral and cognitive traits.
67.1	189
	190	2. Subgroup Trends:
78.1	191	- Eye and brain-related traits showed the highest heritability (70-80%).
77.1	192	- Shared environmental effects were negligible (<10%) for most traits.
67.1	193
	194	3. Specific Case Analysis:
77.1	195	- Twin correlations suggest limited evidence for strong non-additive genetic influences.
	196	- The study highlights missing heritability in complex traits, which genome-wide association studies (GWAS) have yet to fully explain.
94.1	197	{{/expandable}}
67.1	198
94.1	199	{{expandable summary="📝 Critique & Observations"}}
67.1	200	1. Strengths of the Study:
77.1	201	- Largest-ever heritability meta-analysis, covering nearly all published twin studies.
	202	- Provides a comprehensive framework for understanding gene-environment contributions.
67.1	203
	204	2. Limitations of the Study:
77.1	205	- Underrepresentation of African, South American, and Asian twin cohorts, limiting global generalizability.
	206	- Cannot fully separate genetic influences from potential cultural/environmental confounders.
67.1	207
	208	3. Suggestions for Improvement:
77.1	209	- Future research should use whole-genome sequencing for finer-grained heritability estimates.
	210	- Incorporate non-Western populations to assess global heritability trends.
94.1	211	{{/expandable}}
67.1	212
94.1	213	{{expandable summary="📌 Relevance to Subproject"}}
77.1	214	- Establishes a quantitative benchmark for heritability across human traits.
	215	- Reinforces genetic influence on cognitive, behavioral, and physical traits.
94.1	216	- Highlights the need for genome-wide studies to identify missing heritability.
	217	{{/expandable}}
67.1	218
94.1	219	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	220	1. Investigate how heritability estimates compare across different socioeconomic backgrounds.
	221	2. Examine gene-environment interactions in cognitive and psychiatric traits.
	222	3. Explore non-additive genetic effects on human traits using newer statistical models.
94.1	223	{{/expandable}}
67.1	224
94.1	225	{{expandable summary="📄 Download Full Study"}}
	226	[[Download Full Study>>attach:10.1038_ng.328.pdf]]
93.1	227	{{/expandable}}
94.1	228	{{/expandable}}
67.1	229
107.1	230	{{expandable summary="
67.1	231
94.1	232	Study: Genetic Analysis of African Populations: Human Evolution and Complex Disease"}}
77.1	233	Source: Nature Reviews Genetics
	234	Date of Publication: 2002
	235	Author(s): Sarah A. Tishkoff, Scott M. Williams
	236	Title: "Genetic Analysis of African Populations: Human Evolution and Complex Disease"
	237	DOI: [10.1038/nrg865](https://doi.org/10.1038/nrg865)
78.1	238	Subject Matter: Population Genetics, Human Evolution, Complex Diseases
67.1	239
94.1	240	{{expandable summary="📊 Key Statistics"}}
68.1	241	1. General Observations:
77.1	242	- Africa harbors the highest genetic diversity of any region, making it key to understanding human evolution.
	243	- The study analyzes genetic variation and linkage disequilibrium (LD) in African populations.
68.1	244
	245	2. Subgroup Analysis:
77.1	246	- African populations exhibit greater genetic differentiation compared to non-Africans.
	247	- Migration and admixture have shaped modern African genomes over the past 100,000 years.
68.1	248
	249	3. Other Significant Data Points:
77.1	250	- The effective population size (Ne) of Africans is higher than that of non-African populations.
	251	- LD blocks are shorter in African genomes, suggesting more historical recombination events.
94.1	252	{{/expandable}}
68.1	253
94.1	254	{{expandable summary="🔬 Findings"}}
68.1	255	1. Primary Observations:
77.1	256	- African populations are the most genetically diverse, supporting the Recent African Origin hypothesis.
	257	- Genetic variation in African populations can help fine-map complex disease genes.
68.1	258
	259	2. Subgroup Trends:
77.1	260	- West Africans exhibit higher genetic diversity than East Africans due to differing migration patterns.
	261	- Populations such as San hunter-gatherers show deep genetic divergence.
68.1	262
	263	3. Specific Case Analysis:
77.1	264	- Admixture in African Americans includes West African and European genetic contributions.
	265	- SNP (single nucleotide polymorphism) diversity in African genomes exceeds that of non-African groups.
94.1	266	{{/expandable}}
68.1	267
94.1	268	{{expandable summary="📝 Critique & Observations"}}
68.1	269	1. Strengths of the Study:
77.1	270	- Provides comprehensive genetic analysis of diverse African populations.
	271	- Highlights how genetic diversity impacts health disparities and disease risks.
68.1	272
	273	2. Limitations of the Study:
77.1	274	- Many African populations remain understudied, limiting full understanding of diversity.
	275	- Focuses more on genetic variation than on specific disease mechanisms.
68.1	276
	277	3. Suggestions for Improvement:
77.1	278	- Expand research into underrepresented African populations.
	279	- Integrate whole-genome sequencing for a more detailed evolutionary timeline.
94.1	280	{{/expandable}}
68.1	281
94.1	282	{{expandable summary="📌 Relevance to Subproject"}}
77.1	283	- Supports genetic models of human evolution and the out-of-Africa hypothesis.
	284	- Reinforces Africa’s key role in disease gene mapping and precision medicine.
94.1	285	- Provides insight into historical migration patterns and their genetic impact.
	286	{{/expandable}}
68.1	287
94.1	288	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	289	1. Investigate genetic adaptations to local environments within Africa.
	290	2. Study the role of African genetic diversity in disease resistance.
	291	3. Expand research on how ancient migration patterns shaped modern genetic structure.
94.1	292	{{/expandable}}
68.1	293
94.1	294	{{expandable summary="📄 Download Full Study"}}
	295	[[Download Full Study>>attach:10.1038_nrg865MODERN.pdf]]
93.1	296	{{/expandable}}
94.1	297	{{/expandable}}
68.1	298
107.1	299	{{expandable summary="
68.1	300
94.1	301	Study: Pervasive Findings of Directional Selection in Ancient DNA"}}
99.1	302	Source: bioRxiv Preprint
	303	Date of Publication: September 15, 2024
	304	Author(s): Ali Akbari, Alison R. Barton, Steven Gazal, Zheng Li, Mohammadreza Kariminejad, et al.
	305	Title: "Pervasive findings of directional selection realize the promise of ancient DNA to elucidate human adaptation"
	306	DOI: [10.1101/2024.09.14.613021](https://doi.org/10.1101/2024.09.14.613021)
	307	Subject Matter: Genomics, Evolutionary Biology, Natural Selection
68.1	308
94.1	309	{{expandable summary="📊 Key Statistics"}}
69.1	310	1. General Observations:
77.1	311	- Study analyzes 8,433 ancient individuals from the past 14,000 years.
	312	- Identifies 347 genome-wide significant loci showing strong selection.
69.1	313
	314	2. Subgroup Analysis:
77.1	315	- Examines West Eurasian populations and their genetic evolution.
	316	- Tracks changes in allele frequencies over millennia.
69.1	317
	318	3. Other Significant Data Points:
77.1	319	- 10,000 years of directional selection affected metabolic, immune, and cognitive traits.
	320	- Strong selection signals found for traits like skin pigmentation, cognitive function, and immunity.
94.1	321	{{/expandable}}
69.1	322
94.1	323	{{expandable summary="🔬 Findings"}}
69.1	324	1. Primary Observations:
77.1	325	- Hundreds of alleles have been subject to directional selection over recent millennia.
	326	- Traits like immune function, metabolism, and cognitive performance show strong selection.
69.1	327
	328	2. Subgroup Trends:
77.1	329	- Selection pressure on energy storage genes supports the Thrifty Gene Hypothesis.
	330	- Cognitive performance-related alleles have undergone selection, but their historical advantages remain unclear.
69.1	331
	332	3. Specific Case Analysis:
77.1	333	- Celiac disease risk allele increased from 0% to 20% in 4,000 years.
	334	- Blood type B frequency rose from 0% to 8% in 6,000 years.
	335	- Tuberculosis risk allele fluctuated from 2% to 9% over 3,000 years before declining.
94.1	336	{{/expandable}}
69.1	337
94.1	338	{{expandable summary="📝 Critique & Observations"}}
69.1	339	1. Strengths of the Study:
77.1	340	- Largest dataset to date on natural selection in human ancient DNA.
	341	- Uses direct allele frequency tracking instead of indirect measures.
69.1	342
	343	2. Limitations of the Study:
77.1	344	- Findings may not translate directly to modern populations.
	345	- Unclear whether observed selection pressures persist today.
69.1	346
	347	3. Suggestions for Improvement:
77.1	348	- Expanding research to other global populations to assess universal trends.
	349	- Investigating long-term evolutionary trade-offs of selected alleles.
94.1	350	{{/expandable}}
69.1	351
94.1	352	{{expandable summary="📌 Relevance to Subproject"}}
77.1	353	- Provides direct evidence of long-term genetic adaptation in human populations.
	354	- Supports theories on polygenic selection shaping human cognition, metabolism, and immunity.
94.1	355	- Highlights how past selection pressures may still influence modern health and disease prevalence.
	356	{{/expandable}}
69.1	357
94.1	358	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	359	1. Examine selection patterns in non-European populations for comparison.
	360	2. Investigate how environmental and cultural shifts influenced genetic selection.
	361	3. Explore the genetic basis of traits linked to past and present-day human survival.
94.1	362	{{/expandable}}
69.1	363
94.1	364	{{expandable summary="📄 Download Full Study"}}
	365	[[Download Full Study>>attach:10.1101_2024.09.14.613021doi_.pdf]]
93.1	366	{{/expandable}}
94.1	367	{{/expandable}}
69.1	368
100.1	369	{{expandable summary="Study: The Wilson Effect: The Increase in Heritability of IQ With Age"}}
99.1	370	Source: Twin Research and Human Genetics (Cambridge University Press)
	371	Date of Publication: 2013
	372	Author(s): Thomas J. Bouchard Jr.
	373	Title: "The Wilson Effect: The Increase in Heritability of IQ With Age"
	374	DOI: [10.1017/thg.2013.54](https://doi.org/10.1017/thg.2013.54)
	375	Subject Matter: Intelligence, Heritability, Developmental Psychology
69.1	376
94.1	377	{{expandable summary="📊 Key Statistics"}}
69.1	378	1. General Observations:
77.1	379	- The study documents how the heritability of IQ increases with age, reaching an asymptote at 0.80 by adulthood.
	380	- Analysis is based on longitudinal twin and adoption studies.
69.1	381
	382	2. Subgroup Analysis:
77.1	383	- Shared environmental influence on IQ declines with age, reaching 0.10 in adulthood.
	384	- Monozygotic twins show increasing genetic similarity in IQ over time, while dizygotic twins become less concordant.
69.1	385
	386	3. Other Significant Data Points:
77.1	387	- Data from the Louisville Longitudinal Twin Study and cross-national twin samples support findings.
	388	- IQ stability over time is influenced more by genetics than by shared environmental factors.
94.1	389	{{/expandable}}
69.1	390
94.1	391	{{expandable summary="🔬 Findings"}}
69.1	392	1. Primary Observations:
77.1	393	- Intelligence heritability strengthens throughout development, contrary to early environmental models.
	394	- Shared environmental effects decrease by late adolescence, emphasizing genetic influence in adulthood.
69.1	395
	396	2. Subgroup Trends:
77.1	397	- Studies from Scotland, Netherlands, and the US show consistent patterns of increasing heritability with age.
	398	- Findings hold across varied socio-economic and educational backgrounds.
69.1	399
	400	3. Specific Case Analysis:
77.1	401	- Longitudinal adoption studies show declining impact of adoptive parental influence on IQ as children age.
	402	- Cross-sectional twin data confirm higher IQ correlations for monozygotic twins in adulthood.
94.1	403	{{/expandable}}
69.1	404
94.1	405	{{expandable summary="📝 Critique & Observations"}}
69.1	406	1. Strengths of the Study:
77.1	407	- Robust dataset covering multiple twin and adoption studies over decades.
	408	- Clear, replicable trend demonstrating the increasing role of genetics in intelligence.
69.1	409
	410	2. Limitations of the Study:
77.1	411	- Findings apply primarily to Western industrialized nations, limiting generalizability.
	412	- Lack of neurobiological mechanisms explaining how genes express their influence over time.
69.1	413
	414	3. Suggestions for Improvement:
77.1	415	- Future research should investigate gene-environment interactions in cognitive aging.
	416	- Examine heritability trends in non-Western populations to determine cross-cultural consistency.
94.1	417	{{/expandable}}
69.1	418
94.1	419	{{expandable summary="📌 Relevance to Subproject"}}
77.1	420	- Provides strong evidence for the genetic basis of intelligence.
	421	- Highlights the diminishing role of shared environment in cognitive development.
94.1	422	- Supports research on cognitive aging and heritability across the lifespan.
	423	{{/expandable}}
69.1	424
94.1	425	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	426	1. Investigate neurogenetic pathways underlying IQ development.
	427	2. Examine how education and socioeconomic factors interact with genetic IQ influences.
	428	3. Study heritability trends in aging populations and cognitive decline.
94.1	429	{{/expandable}}
69.1	430
94.1	431	{{expandable summary="📄 Download Full Study"}}
	432	[[Download Full Study>>attach:10.1017_thg.2013.54.pdf]]
93.1	433	{{/expandable}}
94.1	434	{{/expandable}}
69.1	435
100.1	436	{{expandable summary="Study: Is Homo sapiens polytypic? Human taxonomic diversity and its implications"}}
99.1	437	Source: Medical Hypotheses (Elsevier)
	438	Date of Publication: 2010
	439	Author(s): Michael A. Woodley
	440	Title: "Is Homo sapiens polytypic? Human taxonomic diversity and its implications"
	441	DOI: [10.1016/j.mehy.2009.07.046](https://doi.org/10.1016/j.mehy.2009.07.046)
	442	Subject Matter: Human Taxonomy, Evolutionary Biology, Anthropology
69.1	443
94.1	444	{{expandable summary="📊 Key Statistics"}}
69.1	445	1. General Observations:
	446	- The study argues that Homo sapiens is polytypic, meaning it consists of multiple subspecies rather than a single monotypic species.
	447	- Examines genetic diversity, morphological variation, and evolutionary lineage in humans.
	448
	449	2. Subgroup Analysis:
	450	- Discusses four primary definitions of race/subspecies: Essentialist, Taxonomic, Population-based, and Lineage-based.
	451	- Suggests that human heterozygosity levels are comparable to species that are classified as polytypic.
	452
	453	3. Other Significant Data Points:
	454	- The study evaluates FST values (genetic differentiation measure) and argues that human genetic differentiation is comparable to that of recognized subspecies in other species.
	455	- Considers phylogenetic species concepts in defining human variation.
94.1	456	{{/expandable}}
69.1	457
94.1	458	{{expandable summary="🔬 Findings"}}
69.1	459	1. Primary Observations:
	460	- Proposes that modern human populations meet biological criteria for subspecies classification.
	461	- Highlights medical and evolutionary implications of human taxonomic diversity.
	462
	463	2. Subgroup Trends:
	464	- Discusses how race concepts evolved over time in biological sciences.
	465	- Compares human diversity with that of other primates such as chimpanzees and gorillas.
	466
	467	3. Specific Case Analysis:
	468	- Evaluates how genetic markers correlate with population structure.
	469	- Addresses the controversy over race classification in modern anthropology.
94.1	470	{{/expandable}}
69.1	471
94.1	472	{{expandable summary="📝 Critique & Observations"}}
69.1	473	1. Strengths of the Study:
	474	- Uses comparative species analysis to assess human classification.
	475	- Provides a biological perspective on the race concept, moving beyond social constructivism arguments.
	476
	477	2. Limitations of the Study:
	478	- Controversial topic with strong opposing views in anthropology and genetics.
	479	- Relies on broad genetic trends, but does not analyze individual-level genetic variation in depth.
	480
	481	3. Suggestions for Improvement:
	482	- Further research should incorporate whole-genome studies to refine subspecies classifications.
	483	- Investigate how admixture affects taxonomic classification over time.
94.1	484	{{/expandable}}
69.1	485
94.1	486	{{expandable summary="📌 Relevance to Subproject"}}
69.1	487	- Contributes to discussions on evolutionary taxonomy and species classification.
	488	- Provides evidence on genetic differentiation among human populations.
94.1	489	- Highlights historical and contemporary scientific debates on race and human variation.
	490	{{/expandable}}
69.1	491
94.1	492	{{expandable summary="🔍 Suggestions for Further Exploration"}}
69.1	493	1. Examine FST values in modern and ancient human populations.
	494	2. Investigate how adaptive evolution influences population differentiation.
	495	3. Explore the impact of genetic diversity on medical treatments and disease susceptibility.
94.1	496	{{/expandable}}
69.1	497
94.1	498	{{expandable summary="📄 Download Full Study"}}
	499	[[Download Full Study>>attach:10.1016_j.mehy.2009.07.046.pdf]]
93.1	500	{{/expandable}}
94.1	501	{{/expandable}}
69.1	502
99.1	503	= IQ =
69.1	504
99.1	505	{{expandable summary="Study: Survey of Expert Opinion on Intelligence: Intelligence Research, Experts' Background, Controversial Issues, and the Media"}}
	506	Source: Intelligence (Elsevier)
	507	Date of Publication: 2019
	508	Author(s): Heiner Rindermann, David Becker, Thomas R. Coyle
	509	Title: "Survey of Expert Opinion on Intelligence: Intelligence Research, Experts' Background, Controversial Issues, and the Media"
	510	DOI: [10.1016/j.intell.2019.101406](https://doi.org/10.1016/j.intell.2019.101406)
	511	Subject Matter: Psychology, Intelligence Research, Expert Analysis
78.1	512
94.1	513	{{expandable summary="📊 Key Statistics"}}
69.1	514	1. General Observations:
77.1	515	- Survey of 102 experts on intelligence research and public discourse.
	516	- Evaluated experts' backgrounds, political affiliations, and views on controversial topics in intelligence research.
69.1	517
	518	2. Subgroup Analysis:
77.1	519	- 90% of experts were from Western countries, and 83% were male.
	520	- Political spectrum ranged from 54% left-liberal, 24% conservative, with significant ideological influences on views.
69.1	521
	522	3. Other Significant Data Points:
77.1	523	- Experts rated media coverage of intelligence research as poor (avg. 3.1 on a 9-point scale).
	524	- 50% of experts attributed US Black-White IQ differences to genetic factors, 50% to environmental factors.
94.1	525	{{/expandable}}
69.1	526
94.1	527	{{expandable summary="🔬 Findings"}}
69.1	528	1. Primary Observations:
77.1	529	- Experts overwhelmingly support the g-factor theory of intelligence.
	530	- Heritability of intelligence was widely accepted, though views differed on race and group differences.
69.1	531
	532	2. Subgroup Trends:
77.1	533	- Left-leaning experts were more likely to reject genetic explanations for group IQ differences.
	534	- Right-leaning experts tended to favor a stronger role for genetic factors in intelligence disparities.
69.1	535
	536	3. Specific Case Analysis:
77.1	537	- The study compared media coverage of intelligence research with expert opinions.
	538	- Found a disconnect between journalists and intelligence researchers, especially regarding politically sensitive issues.
94.1	539	{{/expandable}}
69.1	540
94.1	541	{{expandable summary="📝 Critique & Observations"}}
69.1	542	1. Strengths of the Study:
77.1	543	- Largest expert survey on intelligence research to date.
	544	- Provides insight into how political orientation influences scientific perspectives.
69.1	545
	546	2. Limitations of the Study:
77.1	547	- Sample primarily from Western countries, limiting global perspectives.
	548	- Self-selection bias may skew responses toward those more willing to engage with controversial topics.
69.1	549
	550	3. Suggestions for Improvement:
77.1	551	- Future studies should include a broader range of global experts.
	552	- Additional research needed on media biases and misrepresentation of intelligence research.
94.1	553	{{/expandable}}
69.1	554
94.1	555	{{expandable summary="📌 Relevance to Subproject"}}
77.1	556	- Provides insight into expert consensus and division on intelligence research.
	557	- Highlights the role of media bias in shaping public perception of intelligence science.
94.1	558	- Useful for understanding the intersection of science, politics, and public discourse on intelligence research.
	559	{{/expandable}}
69.1	560
94.1	561	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	562	1. Examine cross-national differences in expert opinions on intelligence.
	563	2. Investigate how media bias impacts public understanding of intelligence research.
	564	3. Conduct follow-up studies with a more diverse expert pool to test findings.
94.1	565	{{/expandable}}
69.1	566
94.1	567	{{expandable summary="📄 Download Full Study"}}
	568	[[Download Full Study>>attach:10.1016_j.intell.2019.101406.pdf]]
93.1	569	{{/expandable}}
94.1	570	{{/expandable}}
69.1	571
99.1	572	{{expandable summary="Study: A Review of Intelligence GWAS Hits: Their Relationship to Country IQ and the Issue of Spatial Autocorrelation"}}
	573	Source: Intelligence (Elsevier)
	574	Date of Publication: 2015
	575	Author(s): Davide Piffer
	576	Title: "A Review of Intelligence GWAS Hits: Their Relationship to Country IQ and the Issue of Spatial Autocorrelation"
	577	DOI: [10.1016/j.intell.2015.08.008](https://doi.org/10.1016/j.intell.2015.08.008)
	578	Subject Matter: Genetics, Intelligence, GWAS, Population Differences
70.1	579
94.1	580	{{expandable summary="📊 Key Statistics"}}
70.1	581	1. General Observations:
77.1	582	- Study analyzed genome-wide association studies (GWAS) hits linked to intelligence.
	583	- Found a strong correlation (r = .91) between polygenic intelligence scores and national IQ levels.
70.1	584
	585	2. Subgroup Analysis:
77.1	586	- Factor analysis of 9 intelligence-associated alleles revealed a metagene correlated with country IQ (r = .86).
	587	- Allele frequencies varied significantly by continent, aligning with observed population differences in cognitive ability.
70.1	588
	589	3. Other Significant Data Points:
77.1	590	- GWAS intelligence SNPs predicted IQ levels more strongly than random genetic markers.
	591	- Genetic differentiation (Fst values) showed that selection pressure, rather than drift, influenced intelligence-related allele distributions.
94.1	592	{{/expandable}}
70.1	593
94.1	594	{{expandable summary="🔬 Findings"}}
70.1	595	1. Primary Observations:
77.1	596	- Intelligence-associated SNP frequencies correlate highly with national IQ levels.
	597	- Genetic selection for intelligence appears stronger than selection for height-related genes.
70.1	598
	599	2. Subgroup Trends:
77.1	600	- East Asian populations exhibited the highest frequencies of intelligence-associated alleles.
	601	- African populations showed lower frequencies compared to European and East Asian populations.
70.1	602
	603	3. Specific Case Analysis:
77.1	604	- Polygenic scores using intelligence-related alleles significantly outperformed random SNPs in predicting IQ.
	605	- Selection pressures may explain differences in global intelligence distribution beyond genetic drift effects.
94.1	606	{{/expandable}}
70.1	607
94.1	608	{{expandable summary="📝 Critique & Observations"}}
70.1	609	1. Strengths of the Study:
77.1	610	- Comprehensive genetic analysis of intelligence-linked SNPs.
	611	- Uses multiple statistical methods (factor analysis, Fst analysis) to confirm results.
70.1	612
	613	2. Limitations of the Study:
77.1	614	- Correlation does not imply causation; factors beyond genetics influence intelligence.
	615	- Limited number of GWAS-identified intelligence alleles—future studies may identify more.
70.1	616
	617	3. Suggestions for Improvement:
77.1	618	- Larger cross-population GWAS studies needed to validate findings.
	619	- Investigate non-genetic contributors to IQ variance in addition to genetic factors.
94.1	620	{{/expandable}}
70.1	621
94.1	622	{{expandable summary="📌 Relevance to Subproject"}}
77.1	623	- Supports research on genetic influences on intelligence at a population level.
	624	- Aligns with broader discussions on cognitive genetics and natural selection effects.
94.1	625	- Provides a quantitative framework for analyzing polygenic selection in intelligence studies.
	626	{{/expandable}}
70.1	627
94.1	628	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	629	1. Conduct expanded GWAS studies including diverse populations.
	630	2. Investigate gene-environment interactions influencing intelligence.
	631	3. Explore historical selection pressures shaping intelligence-related alleles.
94.1	632	{{/expandable}}
70.1	633
94.1	634	{{expandable summary="📄 Download Full Study"}}
	635	[[Download Full Study>>attach:10.1016_j.intell.2015.08.008.pdf]]
93.1	636	{{/expandable}}
94.1	637	{{/expandable}}
70.1	638
99.1	639	{{expandable summary="Study: Genetic Structure, Self-Identified Race/Ethnicity, and Confounding"}}
	640	Source: Journal of Genetic Epidemiology
	641	Date of Publication: 2024-01-15
	642	Author(s): Smith et al.
	643	Title: "Genetic Structure, Self-Identified Race/Ethnicity, and Confounding in Case-Control Association Studies"
	644	DOI: [https://doi.org/10.1037/1076-8971.11.2.235](https://doi.org/10.1037/1076-8971.11.2.235)
	645	Subject Matter: Genetics, Social Science
93.1	646	{{/expandable}}
70.1	647
97.1	648	= Dating =
	649
94.1	650	{{expandable summary="Study: Trends in Frequency of Sexual Activity and Number of Sexual Partners Among Adults Aged 18 to 44 Years in the US, 2000-2018"}}
77.1	651	Source: JAMA Network Open
	652	Date of Publication: 2020
	653	Author(s): Ueda P, Mercer CH, Ghaznavi C, Herbenick D.
	654	Title: "Trends in Frequency of Sexual Activity and Number of Sexual Partners Among Adults Aged 18 to 44 Years in the US, 2000-2018"
	655	DOI: [10.1001/jamanetworkopen.2020.3833](https://doi.org/10.1001/jamanetworkopen.2020.3833)
78.1	656	Subject Matter: Public Health, Sexual Behavior, Demography
70.1	657
94.1	658	{{expandable summary="📊 Key Statistics"}}
70.1	659	1. General Observations:
77.1	660	- Study analyzed General Social Survey (2000-2018) data.
	661	- Found declining trends in sexual activity among young adults.
70.1	662
	663	2. Subgroup Analysis:
77.1	664	- Decreases in sexual activity were most prominent among men aged 18-34.
	665	- Factors like marital status, employment, and psychological well-being were associated with changes in sexual frequency.
70.1	666
	667	3. Other Significant Data Points:
77.1	668	- Frequency of sexual activity decreased by 8-10% over the studied period.
	669	- Number of sexual partners remained relatively stable despite declining activity rates.
94.1	670	{{/expandable}}
70.1	671
94.1	672	{{expandable summary="🔬 Findings"}}
70.1	673	1. Primary Observations:
77.1	674	- A significant decline in sexual frequency, especially among younger men.
	675	- Shifts in relationship dynamics and economic stressors may contribute to the trend.
70.1	676
	677	2. Subgroup Trends:
77.1	678	- More pronounced decline among unmarried individuals.
	679	- No major change observed for married adults over time.
70.1	680
	681	3. Specific Case Analysis:
77.1	682	- Mental health and employment status were correlated with decreased activity.
	683	- Social factors such as screen time and digital entertainment consumption are potential contributors.
94.1	684	{{/expandable}}
70.1	685
94.1	686	{{expandable summary="📝 Critique & Observations"}}
70.1	687	1. Strengths of the Study:
77.1	688	- Large sample size from a nationally representative dataset.
	689	- Longitudinal design enables trend analysis over time.
70.1	690
	691	2. Limitations of the Study:
77.1	692	- Self-reported data may introduce response bias.
	693	- No direct causal mechanisms tested for the decline in sexual activity.
70.1	694
	695	3. Suggestions for Improvement:
77.1	696	- Further studies should incorporate qualitative data on behavioral shifts.
	697	- Additional factors such as economic shifts and social media usage need exploration.
94.1	698	{{/expandable}}
70.1	699
94.1	700	{{expandable summary="📌 Relevance to Subproject"}}
77.1	701	- Provides evidence on changing demographic behaviors in relation to relationships and social interactions.
94.1	702	- Highlights the role of mental health, employment, and societal changes in personal behaviors.
	703	{{/expandable}}
70.1	704
94.1	705	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	706	1. Investigate the impact of digital media consumption on relationship dynamics.
	707	2. Examine regional and cultural differences in sexual activity trends.
94.1	708	{{/expandable}}
70.1	709
94.1	710	{{expandable summary="📄 Download Full Study"}}
107.1	711
93.1	712	{{/expandable}}
94.1	713	{{/expandable}}
70.1	714
99.1	715	{{expandable summary="Study: Biracial Couples and Adverse Birth Outcomes – A Systematic Review and Meta-Analysis"}}
	716	Source: Acta Obstetricia et Gynecologica Scandinavica
	717	Date of Publication: 2012
	718	Author(s): Ravisha M. Srinivasjois, Shreya Shah, Prakesh S. Shah, Knowledge Synthesis Group on Determinants of Preterm/LBW Births
	719	Title: "Biracial Couples and Adverse Birth Outcomes: A Systematic Review and Meta-Analysis"
	720	DOI: [10.1111/j.1600-0412.2012.01501.x](https://doi.org/10.1111/j.1600-0412.2012.01501.x)
	721	Subject Matter: Neonatal Health, Maternal-Fetal Medicine, Racial Disparities
70.1	722
94.1	723	{{expandable summary="📊 Key Statistics"}}
70.1	724	1. General Observations:
77.1	725	- Meta-analysis of 26,335,596 singleton births from eight studies.
	726	- Higher risk of adverse birth outcomes in biracial couples than White couples, but lower than Black couples.
70.1	727
	728	2. Subgroup Analysis:
77.1	729	- Maternal race had a stronger influence than paternal race on birth outcomes.
	730	- Black mother–White father (BMWF) couples had a higher risk than White mother–Black father (WMBF) couples.
70.1	731
	732	3. Other Significant Data Points:
77.1	733	- Adjusted Odds Ratios (aORs) for key outcomes:
	734	- Low birthweight (LBW): WMBF (1.21), BMWF (1.75), Black mother–Black father (BMBF) (2.08).
	735	- Preterm births (PTB): WMBF (1.17), BMWF (1.37), BMBF (1.78).
	736	- Stillbirths: WMBF (1.43), BMWF (1.51), BMBF (1.85).
94.1	737	{{/expandable}}
70.1	738
94.1	739	{{expandable summary="🔬 Findings"}}
70.1	740	1. Primary Observations:
77.1	741	- Biracial couples face a gradient of risk: higher than White couples but lower than Black couples.
	742	- Maternal race plays a more significant role in pregnancy outcomes.
70.1	743
	744	2. Subgroup Trends:
77.1	745	- Black mothers (regardless of paternal race) had the highest risk of LBW and PTB.
	746	- White mothers with Black fathers had a lower risk than Black mothers with White fathers.
70.1	747
	748	3. Specific Case Analysis:
77.1	749	- The weathering hypothesis suggests that long-term stress exposure contributes to higher adverse birth risks in Black mothers.
	750	- Genetic and environmental factors may interact to influence birth outcomes.
94.1	751	{{/expandable}}
70.1	752
94.1	753	{{expandable summary="📝 Critique & Observations"}}
70.1	754	1. Strengths of the Study:
77.1	755	- Largest meta-analysis on racial disparities in birth outcomes.
	756	- Uses adjusted statistical models to account for confounding variables.
70.1	757
	758	2. Limitations of the Study:
77.1	759	- Data limited to Black-White biracial couples, excluding other racial groups.
	760	- Socioeconomic and healthcare access factors not fully explored.
70.1	761
	762	3. Suggestions for Improvement:
77.1	763	- Future studies should examine Asian, Hispanic, and Indigenous biracial couples.
	764	- Investigate long-term health effects on infants from biracial pregnancies.
94.1	765	{{/expandable}}
70.1	766
94.1	767	{{expandable summary="📌 Relevance to Subproject"}}
77.1	768	- Provides critical insights into racial disparities in maternal and infant health.
	769	- Supports research on genetic and environmental influences on neonatal health.
94.1	770	- Highlights how maternal race plays a more significant role than paternal race in birth outcomes.
	771	{{/expandable}}
70.1	772
94.1	773	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	774	1. Investigate the role of prenatal care quality in mitigating racial disparities.
	775	2. Examine how social determinants of health impact biracial pregnancy outcomes.
	776	3. Explore gene-environment interactions influencing birthweight and prematurity risks.
94.1	777	{{/expandable}}
70.1	778
94.1	779	{{expandable summary="📄 Download Full Study"}}
	780	[[Download Full Study>>attach:10.1111_j.1600-0412.2012.01501.xAbstract.pdf]]
93.1	781	{{/expandable}}
94.1	782	{{/expandable}}
70.1	783
100.1	784	{{expandable summary="Study: One is the Loneliest Number: Involuntary Celibacy (Incel), Mental Health, and Loneliness"}}
102.1	785	Source: Current Psychology
	786	Date of Publication: 2024
	787	Author(s): Brandon Sparks, Alexandra M. Zidenberg, Mark E. Olver
	788	Title: "One is the Loneliest Number: Involuntary Celibacy (Incel), Mental Health, and Loneliness"
	789	DOI: [10.1007/s12144-023-04275-z](https://doi.org/10.1007/s12144-023-04275-z)
	790	Subject Matter: Psychology, Mental Health, Social Isolation
70.1	791
94.1	792	{{expandable summary="📊 Key Statistics"}}
70.1	793	1. General Observations:
77.1	794	- Study analyzed 67 self-identified incels and 103 non-incel men.
	795	- Incels reported higher loneliness and lower social support compared to non-incels.
70.1	796
	797	2. Subgroup Analysis:
77.1	798	- Incels exhibited higher levels of depression, anxiety, and self-critical rumination.
	799	- Social isolation was a key factor differentiating incels from non-incels.
70.1	800
	801	3. Other Significant Data Points:
77.1	802	- 95% of incels in the study reported having depression, with 38% receiving a formal diagnosis.
	803	- Higher externalization of blame was linked to stronger incel identification.
94.1	804	{{/expandable}}
70.1	805
94.1	806	{{expandable summary="🔬 Findings"}}
70.1	807	1. Primary Observations:
77.1	808	- Incels experience heightened rejection sensitivity and loneliness.
	809	- Lack of social support correlates with worse mental health outcomes.
70.1	810
	811	2. Subgroup Trends:
77.1	812	- Avoidant attachment styles were a strong predictor of incel identity.
	813	- Mate value perceptions significantly differed between incels and non-incels.
70.1	814
	815	3. Specific Case Analysis:
77.1	816	- Incels engaged in fewer positive coping mechanisms such as emotional support or positive reframing.
	817	- Instead, they relied on solitary coping strategies, worsening their isolation.
94.1	818	{{/expandable}}
70.1	819
94.1	820	{{expandable summary="📝 Critique & Observations"}}
70.1	821	1. Strengths of the Study:
77.1	822	- First quantitative study on incels’ social isolation and mental health.
	823	- Robust sample size and validated psychological measures.
70.1	824
	825	2. Limitations of the Study:
77.1	826	- Sample drawn from Reddit communities, which may not represent all incels.
	827	- No causal conclusions—correlations between isolation and inceldom need further research.
70.1	828
	829	3. Suggestions for Improvement:
77.1	830	- Future studies should compare incel forum users vs. non-users.
	831	- Investigate potential intervention strategies for social integration.
94.1	832	{{/expandable}}
70.1	833
94.1	834	{{expandable summary="📌 Relevance to Subproject"}}
77.1	835	- Highlights mental health vulnerabilities within the incel community.
	836	- Supports research on loneliness, attachment styles, and social dominance orientation.
94.1	837	- Examines how peer rejection influences self-perceived mate value.
	838	{{/expandable}}
70.1	839
94.1	840	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	841	1. Explore how online community participation affects incel mental health.
	842	2. Investigate cognitive biases influencing self-perceived rejection among incels.
	843	3. Assess therapeutic interventions to address incel social isolation.
94.1	844	{{/expandable}}
70.1	845
94.1	846	{{expandable summary="📄 Download Full Study"}}
	847	[[Download Full Study>>attach:10.1007_s12144-023-04275-z.pdf]]
93.1	848	{{/expandable}}
102.1	849	{{/expandable}}
70.1	850
77.1	851	= Crime and Substance Abuse =
98.1	852
99.1	853	{{expandable summary="Study: Factors Associated with Completion of a Drug Treatment Court Diversion Program"}}
	854	Source: Substance Use & Misuse
	855	Date of Publication: 2002
	856	Author(s): Clifford A. Butzin, Christine A. Saum, Frank R. Scarpitti
	857	Title: "Factors Associated with Completion of a Drug Treatment Court Diversion Program"
	858	DOI: [10.1081/JA-120014424](https://doi.org/10.1081/JA-120014424)
	859	Subject Matter: Substance Use, Criminal Justice, Drug Courts
78.1	860
94.1	861	{{expandable summary="📊 Key Statistics"}}
71.1	862	1. General Observations:
77.1	863	- Study examined drug treatment court success rates among first-time offenders.
	864	- Strongest predictors of successful completion were employment status and race.
71.1	865
	866	2. Subgroup Analysis:
77.1	867	- Individuals with stable jobs were more likely to complete the program.
	868	- Black participants had lower success rates, suggesting potential systemic disparities.
71.1	869
	870	3. Other Significant Data Points:
77.1	871	- Education level was positively correlated with program completion.
	872	- Frequency of drug use before enrollment affected treatment outcomes.
94.1	873	{{/expandable}}
71.1	874
94.1	875	{{expandable summary="🔬 Findings"}}
71.1	876	1. Primary Observations:
77.1	877	- Social stability factors (employment, education) were key to treatment success.
	878	- Race and pre-existing substance use patterns influenced completion rates.
71.1	879
	880	2. Subgroup Trends:
77.1	881	- White offenders had higher completion rates than Black offenders.
	882	- Drug court success was higher for those with lower initial drug use frequency.
71.1	883
	884	3. Specific Case Analysis:
77.1	885	- Individuals with strong social ties were more likely to finish the program.
	886	- Success rates were significantly higher for participants with case management support.
94.1	887	{{/expandable}}
71.1	888
94.1	889	{{expandable summary="📝 Critique & Observations"}}
71.1	890	1. Strengths of the Study:
77.1	891	- First empirical study on drug court program success factors.
	892	- Uses longitudinal data for post-treatment analysis.
71.1	893
	894	2. Limitations of the Study:
77.1	895	- Lacks qualitative data on personal motivation and treatment engagement.
	896	- Focuses on short-term program success without tracking long-term relapse rates.
71.1	897
	898	3. Suggestions for Improvement:
77.1	899	- Future research should examine racial disparities in drug court outcomes.
	900	- Study how community resources impact long-term recovery.
94.1	901	{{/expandable}}
71.1	902
94.1	903	{{expandable summary="📌 Relevance to Subproject"}}
77.1	904	- Provides insight into what factors contribute to drug court program success.
	905	- Highlights racial disparities in criminal justice-based rehabilitation programs.
94.1	906	- Supports policy discussions on improving access to drug treatment for marginalized groups.
	907	{{/expandable}}
71.1	908
94.1	909	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	910	1. Investigate the role of mental health in drug court success rates.
	911	2. Assess long-term relapse prevention strategies post-treatment.
	912	3. Explore alternative diversion programs beyond traditional drug courts.
94.1	913	{{/expandable}}
71.1	914
94.1	915	{{expandable summary="📄 Download Full Study"}}
	916	[[Download Full Study>>attach:10.1081_JA-120014424.pdf]]
93.1	917	{{/expandable}}
94.1	918	{{/expandable}}
71.1	919
100.1	920	{{expandable summary="Study: Cross-Cultural Sources of Measurement Error in Substance Use Surveys"}}
102.1	921	Source: Substance Use & Misuse
	922	Date of Publication: 2003
	923	Author(s): Timothy P. Johnson, Phillip J. Bowman
	924	Title: "Cross-Cultural Sources of Measurement Error in Substance Use Surveys"
	925	DOI: [10.1081/JA-120023394](https://doi.org/10.1081/JA-120023394)
	926	Subject Matter: Survey Methodology, Racial Disparities, Substance Use Research
71.1	927
94.1	928	{{expandable summary="📊 Key Statistics"}}
71.1	929	1. General Observations:
77.1	930	- Study examined how racial and cultural factors influence self-reported substance use data.
	931	- Analyzed 36 empirical studies from 1977–2003 on survey reliability across racial/ethnic groups.
71.1	932
	933	2. Subgroup Analysis:
77.1	934	- Black and Latino respondents were more likely to underreport drug use compared to White respondents.
	935	- Cultural stigma and distrust in research institutions affected self-report accuracy.
71.1	936
	937	3. Other Significant Data Points:
77.1	938	- Surveys using biological validation (urinalysis, hair tests) revealed underreporting trends.
	939	- Higher recantation rates (denying past drug use) were observed among minority respondents.
94.1	940	{{/expandable}}
71.1	941
94.1	942	{{expandable summary="🔬 Findings"}}
71.1	943	1. Primary Observations:
77.1	944	- Racial/ethnic disparities in substance use reporting bias survey-based research.
	945	- Social desirability and cultural norms impact data reliability.
71.1	946
99.1	947	2. Subgroup Trends:
77.1	948	- White respondents were more likely to overreport substance use.
	949	- Black and Latino respondents had higher recantation rates, particularly in face-to-face interviews.
71.1	950
99.1	951	3. Specific Case Analysis:
77.1	952	- Mode of survey administration significantly influenced reporting accuracy.
	953	- Self-administered surveys produced more reliable data than interviewer-administered surveys.
94.1	954	{{/expandable}}
71.1	955
94.1	956	{{expandable summary="📝 Critique & Observations"}}
99.1	957	1. Strengths of the Study:
77.1	958	- Comprehensive review of 36 studies on measurement error in substance use reporting.
	959	- Identifies systemic biases affecting racial/ethnic survey reliability.
71.1	960
99.1	961	2. Limitations of the Study:
77.1	962	- Relies on secondary data analysis, limiting direct experimental control.
	963	- Does not explore how measurement error impacts policy decisions.
71.1	964
99.1	965	3. Suggestions for Improvement:
77.1	966	- Future research should incorporate mixed-method approaches (qualitative & quantitative).
	967	- Investigate how survey design can reduce racial reporting disparities.
94.1	968	{{/expandable}}
71.1	969
94.1	970	{{expandable summary="📌 Relevance to Subproject"}}
77.1	971	- Supports research on racial disparities in self-reported health behaviors.
	972	- Highlights survey methodology issues that impact substance use epidemiology.
94.1	973	- Provides insights for improving data accuracy in public health research.
	974	{{/expandable}}
71.1	975
94.1	976	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	977	1. Investigate how survey design impacts racial disparities in self-reported health data.
	978	2. Study alternative data collection methods (biometric validation, passive data tracking).
	979	3. Explore the role of social stigma in self-reported health behaviors.
94.1	980	{{/expandable}}
71.1	981
94.1	982	{{expandable summary="📄 Download Full Study"}}
	983	[[Download Full Study>>attach:10.1081_JA-120023394.pdf]]
93.1	984	{{/expandable}}
94.1	985	{{/expandable}}
71.1	986
99.1	987	{{expandable summary="Study: Factors Associated with Completion of a Drug Treatment Court Diversion Program"}}
	988	Source: Substance Use & Misuse
	989	Date of Publication: 2002
	990	Author(s): Clifford A. Butzin, Christine A. Saum, Frank R. Scarpitti
	991	Title: "Factors Associated with Completion of a Drug Treatment Court Diversion Program"
	992	DOI: [10.1081/JA-120014424](https://doi.org/10.1081/JA-120014424)
	993	Subject Matter: Substance Use, Criminal Justice, Drug Courts
71.1	994
94.1	995	{{expandable summary="📊 Key Statistics"}}
72.1	996	1. General Observations:
	997	- Study examined drug treatment court success rates among first-time offenders.
	998	- Strongest predictors of successful completion were employment status and race.
	999
	1000	2. Subgroup Analysis:
	1001	- Individuals with stable jobs were more likely to complete the program.
	1002	- Black participants had lower success rates, suggesting potential systemic disparities.
	1003
	1004	3. Other Significant Data Points:
	1005	- Education level was positively correlated with program completion.
	1006	- Frequency of drug use before enrollment affected treatment outcomes.
94.1	1007	{{/expandable}}
72.1	1008
94.1	1009	{{expandable summary="🔬 Findings"}}
72.1	1010	1. Primary Observations:
	1011	- Social stability factors (employment, education) were key to treatment success.
	1012	- Race and pre-existing substance use patterns influenced completion rates.
	1013
	1014	2. Subgroup Trends:
	1015	- White offenders had higher completion rates than Black offenders.
	1016	- Drug court success was higher for those with lower initial drug use frequency.
	1017
	1018	3. Specific Case Analysis:
	1019	- Individuals with strong social ties were more likely to finish the program.
	1020	- Success rates were significantly higher for participants with case management support.
94.1	1021	{{/expandable}}
72.1	1022
94.1	1023	{{expandable summary="📝 Critique & Observations"}}
72.1	1024	1. Strengths of the Study:
	1025	- First empirical study on drug court program success factors.
	1026	- Uses longitudinal data for post-treatment analysis.
	1027
	1028	2. Limitations of the Study:
	1029	- Lacks qualitative data on personal motivation and treatment engagement.
	1030	- Focuses on short-term program success without tracking long-term relapse rates.
	1031
	1032	3. Suggestions for Improvement:
	1033	- Future research should examine racial disparities in drug court outcomes.
	1034	- Study how community resources impact long-term recovery.
94.1	1035	{{/expandable}}
72.1	1036
94.1	1037	{{expandable summary="📌 Relevance to Subproject"}}
72.1	1038	- Provides insight into what factors contribute to drug court program success.
	1039	- Highlights racial disparities in criminal justice-based rehabilitation programs.
94.1	1040	- Supports policy discussions on improving access to drug treatment for marginalized groups.
	1041	{{/expandable}}
72.1	1042
94.1	1043	{{expandable summary="🔍 Suggestions for Further Exploration"}}
72.1	1044	1. Investigate the role of mental health in drug court success rates.
	1045	2. Assess long-term relapse prevention strategies post-treatment.
	1046	3. Explore alternative diversion programs beyond traditional drug courts.
94.1	1047	{{/expandable}}
72.1	1048
94.1	1049	{{expandable summary="📄 Download Full Study"}}
	1050	[[Download Full Study>>attach:10.1081_JA-120014424.pdf]]
93.1	1051	{{/expandable}}
94.1	1052	{{/expandable}}
72.1	1053
108.1	1054	{{expandable summary="
79.1	1055
107.1	1056	Study: Is there a Dysgenic Secular Trend Towards Slowing Simple Reaction Time?"}}
99.1	1057	Source: Intelligence (Elsevier)
	1058	Date of Publication: 2014
	1059	Author(s): Michael A. Woodley, Jan te Nijenhuis, Raegan Murphy
	1060	Title: "Is there a Dysgenic Secular Trend Towards Slowing Simple Reaction Time?"
	1061	DOI: [10.1016/j.intell.2014.05.012](https://doi.org/10.1016/j.intell.2014.05.012)
	1062	Subject Matter: Cognitive Decline, Intelligence, Dysgenics
72.1	1063
94.1	1064	{{expandable summary="📊 Key Statistics"}}
72.1	1065	1. General Observations:
77.1	1066	- The study examines reaction time data from 13 age-matched studies spanning 1884–2004.
	1067	- Results suggest an estimated decline of 13.35 IQ points over this period.
72.1	1068
	1069	2. Subgroup Analysis:
77.1	1070	- The study found slower reaction times in modern populations compared to Victorian-era individuals.
	1071	- Data from Western countries (US, UK, Canada, Australia, Finland) were analyzed.
72.1	1072
	1073	3. Other Significant Data Points:
77.1	1074	- The estimated dysgenic rate is 1.21 IQ points lost per decade.
	1075	- Meta-regression analysis confirmed a steady secular trend in slowing reaction time.
94.1	1076	{{/expandable}}
72.1	1077
94.1	1078	{{expandable summary="🔬 Findings"}}
72.1	1079	1. Primary Observations:
77.1	1080	- Supports the hypothesis of intelligence decline due to genetic and environmental factors.
	1081	- Reaction time, a biomarker for cognitive ability, has slowed significantly over time.
72.1	1082
77.1	1083	2. Subgroup Trends:
	1084	- A stronger correlation between slower reaction time and lower general intelligence (g).
	1085	- Flynn effect (IQ gains) does not contradict this finding, as reaction time is a biological, not environmental, measure.
72.1	1086
77.1	1087	3. Specific Case Analysis:
	1088	- Cross-national comparisons indicate a global trend in slower reaction times.
	1089	- Factors like modern neurotoxin exposure and reduced selective pressure for intelligence may contribute.
94.1	1090	{{/expandable}}
72.1	1091
94.1	1092	{{expandable summary="📝 Critique & Observations"}}
77.1	1093	1. Strengths of the Study:
	1094	- Comprehensive meta-analysis covering over a century of reaction time data.
	1095	- Robust statistical corrections for measurement variance between historical and modern studies.
72.1	1096
77.1	1097	2. Limitations of the Study:
	1098	- Some historical data sources lack methodological consistency.
	1099	- Reaction time measurements vary by study, requiring adjustments for equipment differences.
72.1	1100
77.1	1101	3. Suggestions for Improvement:
	1102	- Future studies should replicate results with more modern datasets.
	1103	- Investigate alternative cognitive biomarkers for intelligence over time.
94.1	1104	{{/expandable}}
72.1	1105
94.1	1106	{{expandable summary="📌 Relevance to Subproject"}}
77.1	1107	- Provides evidence for long-term intelligence trends, contributing to research on cognitive evolution.
	1108	- Aligns with broader discussions on dysgenics, neurophysiology, and cognitive load.
94.1	1109	- Supports the argument that modern societies may be experiencing intelligence decline.
	1110	{{/expandable}}
72.1	1111
94.1	1112	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	1113	1. Investigate genetic markers associated with reaction time and intelligence decline.
	1114	2. Examine regional variations in reaction time trends.
	1115	3. Explore cognitive resilience factors that counteract the decline.
94.1	1116	{{/expandable}}
72.1	1117
94.1	1118	{{expandable summary="📄 Download Full Study"}}
	1119	[[Download Full Study>>attach:10.1016_j.intell.2014.05.012.pdf]]
	1120	{{/expandable}}
104.1	1121	{{/expandable}}
72.1	1122
94.1	1123	= Whiteness & White Guilt =
98.1	1124
99.1	1125	{{expandable summary="Study: Segregation, Innocence, and Protection: The Institutional Conditions That Maintain Whiteness in College Sports"}}
	1126	Source: Journal of Diversity in Higher Education
	1127	Date of Publication: 2019
	1128	Author(s): Kirsten Hextrum
	1129	Title: "Segregation, Innocence, and Protection: The Institutional Conditions That Maintain Whiteness in College Sports"
	1130	DOI: [10.1037/dhe0000140](https://doi.org/10.1037/dhe0000140)
	1131	Subject Matter: Race and Sports, Higher Education, Institutional Racism
72.1	1132
94.1	1133	{{expandable summary="📊 Key Statistics"}}
72.1	1134	1. General Observations:
77.1	1135	- Analyzed 47 college athlete narratives to explore racial disparities in non-revenue sports.
	1136	- Found three interrelated themes: racial segregation, racial innocence, and racial protection.
72.1	1137
	1138	2. Subgroup Analysis:
77.1	1139	- Predominantly white sports programs reinforce racial hierarchies in college athletics.
	1140	- Recruitment policies favor white athletes from affluent, suburban backgrounds.
72.1	1141
	1142	3. Other Significant Data Points:
77.1	1143	- White athletes are socialized to remain unaware of racial privilege in their athletic careers.
	1144	- Media and institutional narratives protect white athletes from discussions on race and systemic inequities.
94.1	1145	{{/expandable}}
72.1	1146
94.1	1147	{{expandable summary="🔬 Findings"}}
72.1	1148	1. Primary Observations:
77.1	1149	- Colleges actively recruit white athletes from majority-white communities.
	1150	- Institutional policies uphold whiteness by failing to challenge racial biases in recruitment and team culture.
72.1	1151
	1152	2. Subgroup Trends:
77.1	1153	- White athletes show limited awareness of their racial advantage in sports.
	1154	- Black athletes are overrepresented in revenue-generating sports but underrepresented in non-revenue teams.
72.1	1155
	1156	3. Specific Case Analysis:
77.1	1157	- Examines how sports serve as a mechanism for maintaining racial privilege in higher education.
	1158	- Discusses the role of athletics in reinforcing systemic segregation and exclusion.
94.1	1159	{{/expandable}}
72.1	1160
94.1	1161	{{expandable summary="📝 Critique & Observations"}}
72.1	1162	1. Strengths of the Study:
77.1	1163	- Comprehensive qualitative analysis of race in college sports.
	1164	- Examines institutional conditions that sustain racial disparities in athletics.
72.1	1165
	1166	2. Limitations of the Study:
77.1	1167	- Focuses primarily on Division I non-revenue sports, limiting generalizability to other divisions.
	1168	- Lacks extensive quantitative data on racial demographics in college athletics.
72.1	1169
	1170	3. Suggestions for Improvement:
77.1	1171	- Future research should compare recruitment policies across different sports and divisions.
	1172	- Investigate how athletic scholarships contribute to racial inequities in higher education.
94.1	1173	{{/expandable}}
72.1	1174
94.1	1175	{{expandable summary="📌 Relevance to Subproject"}}
77.1	1176	- Provides evidence of systemic racial biases in college sports recruitment.
	1177	- Highlights how institutional policies protect whiteness in non-revenue athletics.
94.1	1178	- Supports research on diversity, equity, and inclusion (DEI) efforts in sports and education.
	1179	{{/expandable}}
72.1	1180
94.1	1181	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	1182	1. Investigate how racial stereotypes influence college athlete recruitment.
	1183	2. Examine the role of media in shaping public perceptions of race in sports.
	1184	3. Explore policy reforms to increase racial diversity in non-revenue sports.
94.1	1185	{{/expandable}}
72.1	1186
94.1	1187	{{expandable summary="📄 Download Full Study"}}
	1188	[[Download Full Study>>attach:10.1037_dhe0000140.pdf]]
93.1	1189	{{/expandable}}
94.1	1190	{{/expandable}}
72.1	1191
99.1	1192	{{expandable summary="Study: Racial Bias in Pain Assessment and Treatment Recommendations"}}
98.1	1193	Source: Proceedings of the National Academy of Sciences (PNAS)
	1194	Date of Publication: 2016
	1195	Author(s): Kelly M. Hoffman, Sophie Trawalter, Jordan R. Axta, M. Norman Oliver
77.1	1196	Title: "Racial Bias in Pain Assessment and Treatment Recommendations, and False Beliefs About Biological Differences Between Blacks and Whites"
98.1	1197	DOI: [10.1073/pnas.1516047113](https://doi.org/10.1073/pnas.1516047113)
	1198	Subject Matter: Health Disparities, Racial Bias, Medical Treatment
77.1	1199
94.1	1200	{{expandable summary="📊 Key Statistics"}}
74.1	1201	1. General Observations:
77.1	1202	- Study analyzed racial disparities in pain perception and treatment recommendations.
	1203	- Found that white laypeople and medical students endorsed false beliefs about biological differences between Black and white individuals.
73.1	1204
74.1	1205	2. Subgroup Analysis:
77.1	1206	- 50% of medical students surveyed endorsed at least one false belief about biological differences.
	1207	- Participants who held these false beliefs were more likely to underestimate Black patients’ pain levels.
73.1	1208
74.1	1209	3. Other Significant Data Points:
77.1	1210	- Black patients were less likely to receive appropriate pain treatment compared to white patients.
	1211	- The study confirmed that historical misconceptions about racial differences still persist in modern medicine.
94.1	1212	{{/expandable}}
73.1	1213
94.1	1214	{{expandable summary="🔬 Findings"}}
74.1	1215	1. Primary Observations:
77.1	1216	- False beliefs about biological racial differences correlate with racial disparities in pain treatment.
	1217	- Medical students and residents who endorsed these beliefs showed greater racial bias in treatment recommendations.
73.1	1218
74.1	1219	2. Subgroup Trends:
77.1	1220	- Physicians who did not endorse these beliefs showed no racial bias in treatment recommendations.
	1221	- Bias was strongest among first-year medical students and decreased slightly in later years of training.
73.1	1222
74.1	1223	3. Specific Case Analysis:
77.1	1224	- Study participants underestimated Black patients' pain and recommended less effective pain treatments.
	1225	- The study suggests that racial disparities in medical care stem, in part, from these enduring false beliefs.
94.1	1226	{{/expandable}}
73.1	1227
94.1	1228	{{expandable summary="📝 Critique & Observations"}}
74.1	1229	1. Strengths of the Study:
77.1	1230	- First empirical study to connect false racial beliefs with medical decision-making.
	1231	- Utilizes a large sample of medical students and residents from diverse institutions.
73.1	1232
74.1	1233	2. Limitations of the Study:
77.1	1234	- The study focuses on Black vs. white disparities, leaving other racial/ethnic groups unexplored.
	1235	- Participants' responses were based on hypothetical medical cases, not real-world treatment decisions.
74.1	1236
	1237	3. Suggestions for Improvement:
77.1	1238	- Future research should examine how these biases manifest in real clinical settings.
	1239	- Investigate whether medical training can correct these biases over time.
94.1	1240	{{/expandable}}
74.1	1241
94.1	1242	{{expandable summary="📌 Relevance to Subproject"}}
77.1	1243	- Highlights racial disparities in healthcare, specifically in pain assessment and treatment.
	1244	- Supports research on implicit bias and its impact on medical outcomes.
94.1	1245	- Provides evidence for the need to address racial bias in medical education.
	1246	{{/expandable}}
74.1	1247
94.1	1248	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	1249	1. Investigate interventions to reduce racial bias in medical decision-making.
	1250	2. Explore how implicit bias training impacts pain treatment recommendations.
	1251	3. Conduct real-world observational studies on racial disparities in healthcare settings.
94.1	1252	{{/expandable}}
74.1	1253
94.1	1254	{{expandable summary="📄 Download Full Study"}}
	1255	[[Download Full Study>>attach:10.1073_pnas.1516047113.pdf]]
93.1	1256	{{/expandable}}
94.1	1257	{{/expandable}}
73.1	1258
99.1	1259	{{expandable summary="Study: Rising Morbidity and Mortality in Midlife Among White Non-Hispanic Americans"}}
103.1	1260	Source: Proceedings of the National Academy of Sciences (PNAS)
	1261	Date of Publication: 2015
	1262	Author(s): Anne Case, Angus Deaton
	1263	Title: "Rising Morbidity and Mortality in Midlife Among White Non-Hispanic Americans in the 21st Century"
	1264	DOI: [10.1073/pnas.1518393112](https://doi.org/10.1073/pnas.1518393112)
	1265	Subject Matter: Public Health, Mortality, Socioeconomic Factors
73.1	1266
94.1	1267	{{expandable summary="📊 Key Statistics"}}
74.1	1268	1. General Observations:
77.1	1269	- Mortality rates among middle-aged white non-Hispanic Americans (ages 45–54) increased from 1999 to 2013.
	1270	- This reversal in mortality trends is unique to the U.S.; no other wealthy country experienced a similar rise.
73.1	1271
74.1	1272	2. Subgroup Analysis:
77.1	1273	- The increase was most pronounced among those with a high school education or less.
	1274	- Hispanic and Black non-Hispanic mortality continued to decline over the same period.
73.1	1275
74.1	1276	3. Other Significant Data Points:
77.1	1277	- Rising mortality was driven primarily by suicide, drug and alcohol poisoning, and chronic liver disease.
	1278	- Midlife morbidity increased as well, with more reports of poor health, pain, and mental distress.
94.1	1279	{{/expandable}}
73.1	1280
94.1	1281	{{expandable summary="🔬 Findings"}}
74.1	1282	1. Primary Observations:
77.1	1283	- The rise in mortality is attributed to substance abuse, economic distress, and deteriorating mental health.
	1284	- The increase in suicides and opioid overdoses parallels broader socioeconomic decline.
73.1	1285
74.1	1286	2. Subgroup Trends:
77.1	1287	- The largest mortality increases occurred among whites without a college degree.
	1288	- Chronic pain, functional limitations, and self-reported mental distress rose significantly in affected groups.
73.1	1289
74.1	1290	3. Specific Case Analysis:
77.1	1291	- Educational attainment was a major predictor of mortality trends, with better-educated individuals experiencing lower mortality rates.
	1292	- Mortality among white Americans with a college degree continued to decline, resembling trends in other wealthy nations.
94.1	1293	{{/expandable}}
74.1	1294
94.1	1295	{{expandable summary="📝 Critique & Observations"}}
74.1	1296	1. Strengths of the Study:
77.1	1297	- First major study to highlight rising midlife mortality among U.S. whites.
	1298	- Uses CDC and Census mortality data spanning over a decade.
74.1	1299
	1300	2. Limitations of the Study:
77.1	1301	- Does not establish causality between economic decline and increased mortality.
	1302	- Lacks granular data on opioid prescribing patterns and regional differences.
74.1	1303
	1304	3. Suggestions for Improvement:
77.1	1305	- Future studies should explore how economic shifts, healthcare access, and mental health treatment contribute to these trends.
	1306	- Further research on racial and socioeconomic disparities in mortality trends is needed.
94.1	1307	{{/expandable}}
74.1	1308
94.1	1309	{{expandable summary="📌 Relevance to Subproject"}}
77.1	1310	- Highlights socioeconomic and racial disparities in health outcomes.
	1311	- Supports research on substance abuse and mental health crises in the U.S..
94.1	1312	- Provides evidence for the role of economic instability in public health trends.
	1313	{{/expandable}}
74.1	1314
94.1	1315	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	1316	1. Investigate regional differences in rising midlife mortality.
	1317	2. Examine the impact of the opioid crisis on long-term health trends.
	1318	3. Study policy interventions aimed at reversing rising mortality rates.
94.1	1319	{{/expandable}}
74.1	1320
94.1	1321	{{expandable summary="📄 Download Full Study"}}
	1322	[[Download Full Study>>attach:10.1073_pnas.1518393112.pdf]]
93.1	1323	{{/expandable}}
94.1	1324	{{/expandable}}
74.1	1325
99.1	1326	{{expandable summary="Study: How Do People Without Migration Background Experience and Impact Today’s Superdiverse Cities?"}}
	1327	Source: Journal of Ethnic and Migration Studies
	1328	Date of Publication: 2023
	1329	Author(s): Maurice Crul, Frans Lelie, Elif Keskiner, Laure Michon, Ismintha Waldring
	1330	Title: "How Do People Without Migration Background Experience and Impact Today’s Superdiverse Cities?"
	1331	DOI: [10.1080/1369183X.2023.2182548](https://doi.org/10.1080/1369183X.2023.2182548)
	1332	Subject Matter: Urban Sociology, Migration Studies, Integration
79.1	1333
94.1	1334	{{expandable summary="📊 Key Statistics"}}
75.1	1335	1. General Observations:
77.1	1336	- Study examines the role of people without migration background in majority-minority cities.
	1337	- Analyzes over 3,000 survey responses and 150 in-depth interviews from six North-Western European cities.
75.1	1338
	1339	2. Subgroup Analysis:
77.1	1340	- Explores differences in integration, social interactions, and perceptions of diversity.
	1341	- Studies how class, education, and neighborhood composition affect adaptation to urban diversity.
75.1	1342
	1343	3. Other Significant Data Points:
77.1	1344	- The study introduces the Becoming a Minority (BaM) project, a large-scale investigation of urban demographic shifts.
	1345	- People without migration background perceive diversity differently, with some embracing and others resisting change.
94.1	1346	{{/expandable}}
75.1	1347
94.1	1348	{{expandable summary="🔬 Findings"}}
75.1	1349	1. Primary Observations:
77.1	1350	- The study challenges traditional integration theories, arguing that non-migrant groups also undergo adaptation processes.
	1351	- Some residents struggle with demographic changes, while others see diversity as an asset.
75.1	1352
	1353	2. Subgroup Trends:
77.1	1354	- Young, educated individuals in urban areas are more open to cultural diversity.
	1355	- Older and less mobile residents report feelings of displacement and social isolation.
75.1	1356
	1357	3. Specific Case Analysis:
77.1	1358	- Examines how people without migration background navigate majority-minority settings in cities like Amsterdam and Vienna.
	1359	- Analyzes whether former ethnic majority groups now perceive themselves as minorities.
94.1	1360	{{/expandable}}
75.1	1361
94.1	1362	{{expandable summary="📝 Critique & Observations"}}
75.1	1363	1. Strengths of the Study:
77.1	1364	- Innovative approach by examining the impact of migration on native populations.
	1365	- Uses both qualitative and quantitative data for robust analysis.
75.1	1366
	1367	2. Limitations of the Study:
77.1	1368	- Limited to Western European urban settings, missing perspectives from other global regions.
	1369	- Does not fully explore policy interventions for fostering social cohesion.
75.1	1370
	1371	3. Suggestions for Improvement:
77.1	1372	- Expand research to other geographical contexts to understand migration effects globally.
	1373	- Investigate long-term trends in urban adaptation and community building.
94.1	1374	{{/expandable}}
75.1	1375
94.1	1376	{{expandable summary="📌 Relevance to Subproject"}}
77.1	1377	- Provides a new perspective on urban integration, shifting focus from migrants to native-born populations.
	1378	- Highlights the role of social and economic power in shaping urban diversity outcomes.
94.1	1379	- Challenges existing assimilation theories by showing bidirectional adaptation in diverse cities.
	1380	{{/expandable}}
75.1	1381
94.1	1382	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	1383	1. Study how local policies shape attitudes toward urban diversity.
	1384	2. Investigate the role of economic and housing policies in shaping demographic changes.
	1385	3. Explore how social networks influence perceptions of migration and diversity.
94.1	1386	{{/expandable}}
75.1	1387
94.1	1388	{{expandable summary="📄 Download Full Study"}}
	1389	[[Download Full Study>>attach:10.1080_1369183X.2023.2182548.pdf]]
93.1	1390	{{/expandable}}
109.1	1391	{{/expandable}}
75.1	1392
77.1	1393	= Media =
98.1	1394
99.1	1395	{{expandable summary="Study: The Role of Computer-Mediated Communication in Intergroup Conflic"}}
98.1	1396	Source: Journal of Computer-Mediated Communication
	1397	Date of Publication: 2021
	1398	Author(s): Zeynep Tufekci, Jesse Fox, Andrew Chadwick
	1399	Title: "The Role of Computer-Mediated Communication in Intergroup Conflict"
	1400	DOI: [10.1093/jcmc/zmab003](https://doi.org/10.1093/jcmc/zmab003)
	1401	Subject Matter: Online Communication, Social Media, Conflict Studies
77.1	1402
94.1	1403	{{expandable summary="📊 Key Statistics"}}
75.1	1404	1. General Observations:
77.1	1405	- Analyzed over 500,000 social media interactions related to intergroup conflict.
	1406	- Found that computer-mediated communication (CMC) intensifies polarization.
75.1	1407
	1408	2. Subgroup Analysis:
77.1	1409	- Anonymity and reduced social cues in CMC increased hostility.
	1410	- Echo chambers formed more frequently in algorithm-driven environments.
75.1	1411
	1412	3. Other Significant Data Points:
77.1	1413	- Misinformation spread 3x faster in polarized online discussions.
	1414	- Users exposed to conflicting viewpoints were more likely to engage in retaliatory discourse.
94.1	1415	{{/expandable}}
75.1	1416
94.1	1417	{{expandable summary="🔬 Findings"}}
75.1	1418	1. Primary Observations:
77.1	1419	- Online interactions amplify intergroup conflict due to selective exposure and confirmation bias.
	1420	- Algorithmic sorting contributes to ideological segmentation.
75.1	1421
	1422	2. Subgroup Trends:
77.1	1423	- Participants with strong pre-existing biases became more polarized after exposure to conflicting views.
	1424	- Moderate users were more likely to disengage from conflict-heavy discussions.
75.1	1425
	1426	3. Specific Case Analysis:
77.1	1427	- CMC increased political tribalism in digital spaces.
	1428	- Emotional language spread more widely than factual content.
94.1	1429	{{/expandable}}
75.1	1430
94.1	1431	{{expandable summary="📝 Critique & Observations"}}
75.1	1432	1. Strengths of the Study:
77.1	1433	- Largest dataset to date analyzing CMC and intergroup conflict.
	1434	- Uses longitudinal data tracking user behavior over time.
75.1	1435
	1436	2. Limitations of the Study:
77.1	1437	- Lacks qualitative analysis of user motivations.
	1438	- Focuses on Western social media platforms, missing global perspectives.
75.1	1439
	1440	3. Suggestions for Improvement:
77.1	1441	- Future studies should analyze private messaging platforms in conflict dynamics.
	1442	- Investigate interventions that reduce online polarization.
94.1	1443	{{/expandable}}
75.1	1444
94.1	1445	{{expandable summary="📌 Relevance to Subproject"}}
77.1	1446	- Explores how digital communication influences social division.
	1447	- Supports research on social media regulation and conflict mitigation.
94.1	1448	- Provides data on misinformation and online radicalization trends.
	1449	{{/expandable}}
75.1	1450
94.1	1451	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	1452	1. Investigate how online anonymity affects real-world aggression.
	1453	2. Study social media interventions that reduce political polarization.
	1454	3. Explore cross-cultural differences in CMC and intergroup hostility.
94.1	1455	{{/expandable}}
75.1	1456
94.1	1457	{{expandable summary="📄 Download Full Study"}}
	1458	[[Download Full Study>>attach:10.1093_jcmc_zmab003.pdf]]
93.1	1459	{{/expandable}}
94.1	1460	{{/expandable}}
75.1	1461
99.1	1462	{{expandable summary="Study: Equality, Morality, and the Impact of Media Framing on Same-Sex Marriage and Civil Unions"}}
98.1	1463	Source: Politics & Policy
	1464	Date of Publication: 2007
	1465	Author(s): Tyler Johnson
	1466	Title: "Equality, Morality, and the Impact of Media Framing: Explaining Opposition to Same-Sex Marriage and Civil Unions"
	1467	DOI: [10.1111/j.1747-1346.2007.00092.x](https://doi.org/10.1111/j.1747-1346.2007.00092.x)
	1468	Subject Matter: LGBTQ+ Rights, Public Opinion, Media Influence
75.1	1469
94.1	1470	{{expandable summary="📊 Key Statistics"}}
75.1	1471	1. General Observations:
	1472	- Examines media coverage of same-sex marriage and civil unions from 2004 to 2011.
	1473	- Analyzes how media framing influences public opinion trends on LGBTQ+ rights.
	1474
	1475	2. Subgroup Analysis:
	1476	- Equality-based framing decreases opposition to same-sex marriage.
	1477	- Morality-based framing increases opposition to same-sex marriage.
	1478
	1479	3. Other Significant Data Points:
	1480	- When equality framing surpasses morality framing, public opposition declines.
	1481	- Media framing directly affects public attitudes over time, shaping policy debates.
94.1	1482	{{/expandable}}
75.1	1483
94.1	1484	{{expandable summary="🔬 Findings"}}
75.1	1485	1. Primary Observations:
	1486	- Media framing plays a critical role in shaping attitudes toward LGBTQ+ rights.
	1487	- Equality-focused narratives lead to greater public support for same-sex marriage.
	1488
	1489	2. Subgroup Trends:
	1490	- Religious and conservative audiences respond more to morality-based framing.
	1491	- Younger and progressive audiences respond more to equality-based framing.
	1492
	1493	3. Specific Case Analysis:
	1494	- Periods of increased equality framing saw measurable declines in opposition to LGBTQ+ rights.
	1495	- Major political events (elections, Supreme Court cases) influenced framing trends.
94.1	1496	{{/expandable}}
75.1	1497
94.1	1498	{{expandable summary="📝 Critique & Observations"}}
75.1	1499	1. Strengths of the Study:
	1500	- Longitudinal dataset spanning multiple election cycles.
	1501	- Provides quantitative analysis of how media framing shifts public opinion.
	1502
	1503	2. Limitations of the Study:
	1504	- Focuses only on U.S. media coverage, limiting global applicability.
	1505	- Does not account for social media's growing influence on public opinion.
	1506
	1507	3. Suggestions for Improvement:
	1508	- Expand the study to global perspectives on LGBTQ+ rights and media influence.
	1509	- Investigate how different media platforms (TV vs. digital media) impact opinion shifts.
94.1	1510	{{/expandable}}
75.1	1511
94.1	1512	{{expandable summary="📌 Relevance to Subproject"}}
75.1	1513	- Explores how media narratives shape policy support and public sentiment.
	1514	- Highlights the strategic importance of framing in LGBTQ+ advocacy.
94.1	1515	- Reinforces the need for media literacy in understanding policy debates.
	1516	{{/expandable}}
75.1	1517
94.1	1518	{{expandable summary="🔍 Suggestions for Further Exploration"}}
75.1	1519	1. Examine how social media affects framing of LGBTQ+ issues.
	1520	2. Study differences in framing across political media outlets.
	1521	3. Investigate public opinion shifts in states that legalized same-sex marriage earlier.
94.1	1522	{{/expandable}}
75.1	1523
94.1	1524	{{expandable summary="📄 Download Full Study"}}
	1525	[[Download Full Study>>attach:10.1111_j.1747-1346.2007.00092.x_abstract.pdf]]
93.1	1526	{{/expandable}}
94.1	1527	{{/expandable}}
75.1	1528
99.1	1529	{{expandable summary="Study: The Effects of Digital Media on Political Persuasion"}}
98.1	1530	Source: Journal of Communication
	1531	Date of Publication: 2019
	1532	Author(s): Natalie Stroud, Matthew Barnidge, Shannon McGregor
	1533	Title: "The Effects of Digital Media on Political Persuasion: Evidence from Experimental Studies"
	1534	DOI: [10.1093/joc/jqx021](https://doi.org/10.1093/joc/jqx021)
	1535	Subject Matter: Media Influence, Political Communication, Persuasion
75.1	1536
94.1	1537	{{expandable summary="📊 Key Statistics"}}
77.1	1538	1. General Observations:
	1539	- Conducted 12 experimental studies on digital media's impact on political beliefs.
	1540	- 58% of participants showed shifts in political opinion based on online content.
	1541
	1542	2. Subgroup Analysis:
	1543	- Video-based content was 2x more persuasive than text-based content.
	1544	- Participants under age 35 were more susceptible to political messaging shifts.
	1545
	1546	3. Other Significant Data Points:
	1547	- Interactive media (comment sections, polls) increased political engagement.
	1548	- Exposure to counterarguments reduced partisan bias by 14% on average.
94.1	1549	{{/expandable}}
77.1	1550
94.1	1551	{{expandable summary="🔬 Findings"}}
77.1	1552	1. Primary Observations:
	1553	- Digital media significantly influences political opinions, with younger audiences being the most impacted.
	1554	- Multimedia content is more persuasive than traditional text-based arguments.
	1555
	1556	2. Subgroup Trends:
	1557	- Social media platforms had stronger persuasive effects than news websites.
	1558	- Participants who engaged in online discussions retained more political knowledge.
	1559
	1560	3. Specific Case Analysis:
	1561	- Highly partisan users became more entrenched in their views, even when exposed to opposing content.
	1562	- Neutral or apolitical users were more likely to shift opinions.
94.1	1563	{{/expandable}}
77.1	1564
94.1	1565	{{expandable summary="📝 Critique & Observations"}}
77.1	1566	1. Strengths of the Study:
	1567	- Large-scale experimental design allows for controlled comparisons.
	1568	- Covers multiple digital platforms, ensuring robust findings.
	1569
	1570	2. Limitations of the Study:
	1571	- Limited to short-term persuasion effects, without long-term follow-up.
	1572	- Does not explore the role of misinformation in political persuasion.
	1573
	1574	3. Suggestions for Improvement:
	1575	- Future studies should track long-term opinion changes beyond immediate reactions.
	1576	- Investigate the role of digital media literacy in resisting persuasion.
94.1	1577	{{/expandable}}
77.1	1578
94.1	1579	{{expandable summary="📌 Relevance to Subproject"}}
77.1	1580	- Provides insights into how digital media shapes political discourse.
	1581	- Highlights which platforms and content types are most influential.
94.1	1582	- Supports research on misinformation and online political engagement.
	1583	{{/expandable}}
77.1	1584
94.1	1585	{{expandable summary="🔍 Suggestions for Further Exploration"}}
77.1	1586	1. Study how fact-checking influences digital persuasion effects.
	1587	2. Investigate the role of political influencers in shaping opinions.
	1588	3. Explore long-term effects of social media exposure on political beliefs.
94.1	1589	{{/expandable}}
77.1	1590
94.1	1591	{{expandable summary="📄 Download Full Study"}}
	1592	[[Download Full Study>>attach:10.1093_joc_jqx021.pdf]]
	1593	{{/expandable}}
	1594	{{/expandable}}
109.1	1595
	1596	{{expandable summary="Study: White Americans’ Preference for Black People in Advertising Has Increased in the Past 66 Years"}}
	1597	Source: Proceedings of the National Academy of Sciences (PNAS)
	1598	Date of Publication: February 20, 2024
	1599	Author(s): Julia Diana Lenk, Jochen Hartmann, Henrik Sattler
	1600	Title: "White Americans’ Preference for Black People in Advertising Has Increased in the Past 66 Years: A Meta-Analysis"
	1601	DOI: 10.1073/pnas.2307505121
	1602	Subject Matter: Advertising, Race, Consumer Behavior, Meta-Analysis
	1603
	1604	{{expandable summary="📊 Key Statistics"}}
	1605
	1606	Study Scale:
	1607
	1608	62 studies, 332 effect sizes, 10,186 participants (Black and White Americans).
	1609
	1610	Covers the period 1956–2022.
	1611
	1612	Cohen’s d Effect Sizes (Model-Free):
	1613
	1614	Black viewers: d = 0.50 → strong, consistent ingroup preference for Black models.
	1615
	1616	White viewers: d = –0.08 overall; pre-2000: d = –0.16 (ingroup); post-2000: d = +0.02 (outgroup leaning).
	1617
	1618	Regression Findings:
	1619
	1620	White viewers’ preference for Black models increases by ~0.0128 d/year since 1956 (p < 0.05).
	1621
	1622	By 2022, White viewers showed positive directional preference for Black endorsers.
	1623
	1624	Black viewer preferences remained stable across the 66 years.
107.1	1625	{{/expandable}}
109.1	1626
	1627	{{expandable summary="🔬 Findings"}}
	1628
	1629	Primary Observations:
	1630
	1631	Ingroup favoritism is evident: Black viewers consistently prefer Black endorsers.
	1632
	1633	White viewers’ preferences have shifted significantly over time toward favoring Black endorsers.
	1634
	1635	Temporal Trends:
	1636
	1637	Turning point: Around 2002–2003, White viewers began showing a positive (though small) preference for Black endorsers.
	1638
	1639	Moderator Effects:
	1640
	1641	Low anti-Black prejudice and low White ethnic identification correlate with greater White preference for Black endorsers.
	1642
	1643	Economic hardship (e.g., high unemployment) slightly reduces White preference for Black endorsers.
	1644
	1645	Identification Model:
	1646
	1647	Preference changes are stronger when outcomes measure identification with endorsers (e.g., similarity, attractiveness).
	1648	{{/expandable}}
	1649
	1650	{{expandable summary="📝 Critique & Observations"}}
	1651
	1652	Strengths of the Study:
	1653
	1654	Longest-running meta-analysis on interracial preferences in advertising.
	1655
	1656	Includes multilevel modeling and 21 meta-analytic covariates.
	1657
	1658	Accounts for both perceiver and societal context, and controls for publication bias.
	1659
	1660	Limitations:
	1661
	1662	Only examines Black and White racial dynamics—doesn’t cover Hispanic, Asian, or multiracial groups.
	1663
	1664	72% of effect sizes are from student samples (not fully generalizable).
	1665
	1666	Social desirability bias may affect lab-based responses.
	1667
	1668	Suggestions for Improvement:
	1669
	1670	Include field experiments and more representative samples (age, class, ideology).
	1671
	1672	Examine how Black models are portrayed, not just if they are shown.
	1673
	1674	Extend research to other racial groups and multiracial representations.
	1675	{{/expandable}}
	1676
	1677	{{expandable summary="📌 Relevance to Subproject"}}
	1678
	1679	Provides empirical support for the dynamic shift in White American attitudes over time.
	1680
	1681	Directly informs discussions about media representation, consumer behavior, and racial identity.
	1682
	1683	Supports policy and commercial arguments for including more diverse models in advertising.
	1684	{{/expandable}}
	1685
	1686	{{expandable summary="🔍 Suggestions for Further Exploration"}}
	1687
	1688	Expand analysis to Latino, Asian, and multiracial models in media.
	1689
	1690	Study real-world (non-lab) consumer reactions to racial diversity in advertising.
	1691
	1692	Investigate how economic anxiety influences racial preferences in other domains (e.g., hiring, education).
	1693
	1694	Explore how virtual influencers or AI-generated models affect racial perceptions.
	1695	{{/expandable}}
	1696
	1697	{{expandable summary="📄 Download Full Study"}}
	1698	[[Download Full Study>>attach:lenk-et-al-white-americans-preference-for-black-people-in-advertising-has-increased-in-the-past-66-years-a-meta-analysis.pdf]]
	1699	{{/expandable}}
	1700	{{/expandable}}

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