Bibliographic record
Abstract
Contemporary conflicts, asymmetric conflicts, or New Wars as they are now called differ in nature and context from earlier, traditional, or Old Wars. As a result, the effects of these New Wars on women have also altered in various ways. However, when we say that women are suffering in conflicts nowadays, it does not negate their suffering in earlier or traditional wars. The assertion here is that because of the changing nature of conflicts, more civilians, and therefore an increasing number of women and children, are being negatively affected than in the traditional forms of war.This paper will look into how New Wars have made an impact on the lives of women and how they have been rendered more vulnerable as a result. It will also look at the ways in which women have worked towards bringing about positive changes in their societies and tried to influence their governments to prevent violence and work towards sustainable peace. Examples from Jammu and Kashmir will be analyzed to show how women’s groups from across the Line of Control (LoC) between India and Pakistan have come together to build a platform for people-to-people interaction, reduce stereotypes of the ‘Other’ and focus on arriving at a common ground. Individual case studies of women having moved beyond victimhood will be highlighted to show how women can make a positive impact and act as role models.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".