Bibliographic record
Abstract
Intimate partner violence (IPV) is a serious and prevalent issue throughout the world (Devries et al. 2013, 1527). IPV takes place within an intersectional context that includes race, gender, culture, power, and sexuality. The types of actions taken to combat this violence vary greatly between different cultural contexts. The United States and Canada frequently take a law-based approach toward dealing with perpetrators and attempt to assist the victims through various social service sectors. Countries of reconciliation, such as Senegal, Trinidad and Tobago, and Kazakhstan, where individuals wish to keep the issue in the private sphere, often employ an approach aimed at maintaining the family system. Acts of reconciliation as a response to IPV have been deemed as inappropriate and oppressive reactions to the violence perpetuated against the victims (Coker, 2002; London, 1997). However, this criticism neglects the cleawr intersection of IPV responses and cultural contexts, thereby neglecting the autonomy of the woman to choose the response she believes most aligns with her cultural values. Through its emphasis on family, hospitality, respect, as well as religious texts and parables, the Society of Muslim Women (SMW) in Kazakhstan provides an example of a culturally and gender-appropriate reconciliation process. With the example of Kazakhstan, this paper shows that the reconciliation approach can allow the autonomy and cultural values of the female victim to be appreciated.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".