Bibliographic record
Abstract
'Honour', an undefined notion in a patriarchal society like Pakistan, is used as a tool to justify the crime of murder. Violence in the name of honour is not a new phenomenon. Historically, it has been justified in the name of culture but the scope of this tradition has broadened with time and there is an enormous increase in the number of its victims. This cultural notion is interpreted in a way to control women's sexuality and to keep women subordinate to men. Honour killing is not legally sanctioned but the judiciary, the administration and the society often condone it one way or the other. In the tribal areas of Pakistan where such murder is not considered a crime, honour killing is a punishment for those who contravene against the traditional honour code. The wide acceptance of honour killing has made women suffer as a whole against their basic rights; human, constitutional and Islamic. This thesis focuses on the judicial redress against the crime of honour killings, which could be achieved by proper administration of justice. It contests that to control the crime in the patriarchal society of Pakistan, legislative measures are not enough. There is a dire need to eliminate the inadequacies of the administration of justice. The State could build a judicial framework to eliminate the inequality and discrimination against women. The judiciary could play an important role in bringing justice to the victims and in curbing this heinous crime.
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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 | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".