Bibliographic record
Abstract
EDITORS' INTRODUCTION Suicide and violence are both culturally determined and influenced. There is considerable evidence that rates of suicide vary dramatically across nations, and cultures deal with these acts in different manners. The relationship between mental illness and suicide also varies. In some cultures, such as China and Sri Lanka, the rates of suicide are very high, but the rates of mental illness among those committing suicide are not. Social factors such as education, employment, high aspirations and poverty, along with stressors such as life events, may play a role. In some societies, the act of suicide remains illegal; therefore it is impossible to get accurate rates of suicide. Violence is related to a number of similar factors and globalization and urbanization may play an important role. Gender differences in suicide and violence vary too. In this chapter, Tousignant and Laliberté propose that the national and gender differences in suicide and violence are culturally determined. Marital conflicts and relationship problems with in-laws are common causes of domestic violence and dowry deaths are sometimes passed off as suicide or accidental deaths. Embedded within these acts are the gender role and gender-role expectations. Using examples from aboriginal groups for rates of suicide and in Quebec, Tousignant and Laliberté suggest that drug or alcohol problems, along with problems in attachments and problems in relationships and breakdown of relationships, produce inordinate pressure on individuals, which is used as a trigger for seeking a way out. The sociocultural model these authors put forward is important in understanding vulnerability factors, which are more likely to be specific for specific groups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".