Bibliographic record
Abstract
Across a range of activities, gender often appears to be a significant predictor of attitudes and social behaviour. Male traits of risk-taking, competitiveness, reluctance to reveal feelings and abdication of primary care tasks to women show up in patterns of loss and reactions to bereavement. According to Field et al . (1997) men are more likely to die from cardiovascular illness, suicide, murder, accident and warfare. In 1999 in the UK, one in five males aged 16–24 was a victim of violence compared with one in ten females of the equivalent age. Men are three times more likely to die through taking their own lives than women. Men die younger than women. In 1997, nearly two out of every three people aged 75 and over in the UK were women. A quarter of all families are headed by a single parent, practically all of whom are women (Gibson, 2001). Women appear to be at greater risk from domestic violence, though, typically, figures reflecting the ‘true’ picture of the victimization of men by female partners are notoriously difficult to obtain. With all victimization, as with mental health problems, men are less likely than women to admit a problem, to seek help or to visit a doctor. Women’s lives are more open to surveillance by the medical profession. They are two and a half times more likely to be treated for depression than men (Gibson, 2001). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".