Bibliographic record
Abstract
Researchers from the Philippines, Mexico, Italy, Germany, Chile, Canada, Brazil, China, and the US shed new light on important questions in lesbian psychology while subverting the hegemonic status of Western scholarship. Articles part of this special issue move away from treating LGBTQ + identity as a monolith and center lesbian identity. An eclectic set of contributions explore central questions in the field of psychology, including differences between gay men's and lesbian women's mental health as well as similarities and differences between bisexual and lesbian women's sense of identity. This special issue pushes the field to consider how cultural values such as collectivism and individualism, religious affiliation, and the intersections of misogyny and homophobia configure the risk of mental health problems, intimate partner violence, and body dissatisfaction among lesbian women.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".