Bibliographic record
Abstract
Gender identity is often conceptualized in a dichotomous manner: a person’s basic sense of self as either male or female. Of course, it is not necessary, theoretically or empirically, to restrict the conceptualization in this way. One can imagine “third” (or even more) gender identities. Indeed, in recent years, some adults have characterized their subjective gender identity as “intersexed” (Schober, 2001). However, the vast majority of humans appear to differentiate rather strongly either a male or a female gender identity. The origins and determinants of gender identity differentiation have long elicited rather pointed controversy and debate. The tendency to dichotomize the reason as either fully biological (the essentialist perspective) or psychosocial (the constructionist or socialization perspective) is well illuminated by the following two quotes: On the one hand, Swaab et al. (1992) asserted that gender identity is very difficult to change, “probably because...[it is] fixed in the brain” (p. 52). On the other hand, Thorne (1993) asserted that:
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".