Is everyone a mix of straight and gay? A social pressure theory of sexual orientation, with supporting data from a large global sample
Bibliographic record
Abstract
Sigmund Freud, Alfred Kinsey, E.O. Wilson, and others have suggested that social pressure suppresses natural tendencies for humans to express bisexuality, the apparent norm for one of our two closest genetic relatives, the bonobo. An analysis of data obtained from a new online sample of 1,150,938 people in 215 countries and territories (63.9% from the United States, United Kingdom, and Canada) who completed the English version of a validated questionnaire of sexual orientation lends support to this idea. A histogram of scores from 0 (exclusive opposite-sex inclinations) to 18 (exclusive same-sex inclinations) forms a near-normal distribution. Although this distribution was likely caused to some extent by sampling bias, it may also reflect the unusual honesty people show when taking online tests anonymously, as an increasing body of evidence demonstrates. We present a formal mathematical expression of a social pressure theory of sexual orientation, along with empirical evidence and computational explorations that support the theory. We also present an analysis of the new data set. Among other findings: sexual orientation labels corresponded to broad, skewed, overlapping distributions of scores. Self-labeled gays/lesbians and, to a greater extent, self-labeled straights, reported that the larger the mismatch between their sexual orientation label and their actual sexual inclinations, the more distress they felt regarding their sexual orientation, a finding that is predictable from cognitive dissonance theory. Educating the public about the true nature of sexual orientation might quell the often rancorous public debates on this topic, as well as give comfort to a large number of mislabeled people.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".