Legacy of Honor and Violence: An Analysis of Factors Responsible for Honor Killings in Afghanistan, Canada, India, and Pakistan as Discussed in Selected Documentaries on Real Cases
Bibliographic record
Abstract
The present study scrutinizes the cases of honor killings in Afghanistan, India, Pakistan, and Canada through selected documentary films. The case focuses on the social, moral, and religious aspects that coerce some people to take the lives of their own family members in case they defy norms. The documentaries chosen as case studies provide the perspectives of both the victims and the victimizers regarding the concepts of honor, dishonor, and honor killings. People in certain societies reject progressive new thought as attempts to contaminate their perceived cultural purity. People from these communities who try to assimilate liberal ideas are often shunned, especially when the emancipation of women is concerned. Even the seemingly progressive males are very unforgiving about the female members of their families embracing the modern ways of life. The women who try to defy set traditions are branded as being rebellious and are punished to serve as a precedent for future rebellions by women and to save society from their alleged bad influence. In some patriarchal societies, women are seen as the preservers of the family’s honor, and their conduct often reflects the family’s culture, morality, and ethics. Any lapse on a woman’s part allegedly taints the family’s name, and punishment must be given to the erring party to restore the family’s honor. The case also studies the influence of society as a compelling factor in honor killings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".