Bibliographic record
Abstract
Violence in general and violence against women in particular, is a global phenomenon rooted what the wind would seem to study this phenomenon and ways of dealing with it .With that in mind a few tips outlined can be downloaded This requires a comprehensive review of the planning area is adequate visibility For example, the fact that violence against women in Iran for several reasons False belief, education, social issues, often concealing the Most complete and comprehensive statistics on violence are Violence against women in both the public and private domains will apply .In other words, the public sector together with a set of economic, cultural , religious, political , familial , legal , psychological , social and face Each has a large impact on these behaviors are And in the private sphere of the family and the wife dealing with agents In other words, it is the main component and it can be called domestic violence .The main strategy for the prevention of violence against women can be the first in summary, the general concept of women's empower me.Their empowerment in terms of self-confidence, social skills , such as learning to say no and ..., Economic potential , its scientific and ... The next step should be the role of legislative bodies to Situation more and more laws to protect women against violence noted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.973 | 0.956 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".