Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it