Bibliographic record
Abstract
This essay overviews some of the major themes that have emerged from the sociological study of sexual identities (e.g., heterosexual/straight and LGBQ (lesbian, gay, bisexual, and queer)) over the past several decades. Only a minority of countries have representative data available about sexual identification, although it is now available for the first time in certain countries, including Brazil and Japan. Although the prevalence of LGBQ identification varies, it is far higher in the Unites States than anywhere else, and the gender gap is larger as well. The primary reason why the Unites States is an outlier is because young American women have such high rates of LGBQ, and especially bisexual, identification. A uniquely strong link between left-wing ideology and LGBQ identity in the Unites States may help explain this trend. Other work has examined identity-behavior discordance among heterosexuals, the demography of emerging sexual identities such as asexual, and secondary sexual identities such as “daddy” and “bear” among gay men. While there is extensive work about LGBQ life in the Unites States and to a lesser extent in other parts of the Western world, LGBQ life in other global regions is underrepresented in the literature.
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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 | 0.032 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 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".