“Mostly White, heterosexual couples”: Examining demographic diversity and reporting practices in relationship science research samples.
Bibliographic record
Abstract
, a major subfield of social-personality psychology, illustrating both the unique diversity-relevant challenges faced by particular subfields and the barriers to inclusive and diverse research that are shared across research areas. Specifically, we examine the sample diversity and reporting practices of 1,762 studies published in eight mainstream psychology and relationships journals at two time points-(a) 1996-2000 and (b) 2016-2020-and center our analysis around five focal sample characteristics: gender, sexual orientation, regional context, socioeconomic status (SES), and race. We find that reporting practices and representation have not improved for some core demographic characteristics (e.g., socioeconomic status) and that even in domains for which reporting practices have improved (e.g., sexual orientation), reporting remains limited. Further, we find that reporting practices in relationship science frequently center Whiteness (e.g., "participants were mostly White"), obscure or overlook potential sexual orientation diversity (e.g., implying that individuals in man-woman dyads are "heterosexual"), and treat the United States as the contextual default (e.g., participants came from a "large Southeastern university"). In light of these findings, we offer recommendations that we hope will cultivate a more representative and inclusive discipline. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".