Bibliographic record
Abstract
Men and psychology have a somewhat awkward relationship. From its earliest days in the last quarter of the nineteenth century until its mature age in the 1960s, psychology was largely concerned with studying one half of humankind: men. The people who provided the data by participating in psychological experiments were mostly of the male sex, and so was the majority of psychologists reporting about these experiments. As a matter of consequence, a male bias could be discerned in the theories that were advocated, and the topics that were investigated. But, despite the overrepresentation of men in psychology, men were hardly ever studied as men . They were generally seen as representatives of the human species and treated as if they had no gender (Kimmel & Messner, 1989). Ironically, it took the feminist criticism of the male bias in psychology before a substantial psychology of men was developed. In the 1970s a number of psychologists pioneered in this new field using bits of psychological knowledge to understand masculinity. Most of them were concerned with consciousness raising in accordance with the political aims of their feminist sisters: emancipation required as many personal and structural changes in the lives of men as in women's lives. In academic research, psychologists undertook empirical analyses of the vicissitudes of the male role. In clinical settings, new therapies were developed that confronted men with their personal behavior under patriarchy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".