Existe-t-il un consensus social pour définir et comprendre la problématique de la violence conjugale?
Bibliographic record
Abstract
Why is that some relationships, initially harmonious, tip into violence and abuse? Many studies have examined the phenomenon to attempt to circumscribe the incidence and seize its dynamics. These studies seem to bring out a social phenomenon of quite serious amplitude. Thus, according to a study conducted by MacLeod and Cadieux in 1980, one in ten women is battered on a regular basis. According to Statistics Canada, in 1993, 25% of Canadian women have been victim of violence by their partner since the age of sixteen. Among this group, 15% are still living with the same partner. Moreover, despite programs destined to help victims of conjugal violence, the number of cases declared appears to have diminished. Such alarming results have brought many researchers to study the problem further. In the last ten years, some progress has thus been realized in the understanding of the phenomenon of violence against women. Programs of intervention, government involvement, judiciarization of certain forms of abuse, sensitization of the public regarding conjugal violence as well as denunciation of cases of violence have marked this progress. In spite of a rising conscience regarding conjugal violence, research sometimes runs up against obstacles. In spite of complex modelization of concepts and factors of prediction of the phenomenon, results sometimes appear disappointing. Thus, is there a social consensus on a definition of the problem and its dynamics? The authors will try to answer this question by reviewing different theoretical approaches used to define conjugal violence. They will then attempt to make a critical analysis of these theories by examining different empirical studies realized in this field.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".