Women in Afghanistan: Passive victims of the borga or active social participants?
Bibliographic record
Abstract
This paper, based on field research in Kabul in February 2002, begins by discussing how women experience war and violent conflict differently from men, in particular by defining different types of violence against women in Afghanistan. Second, by identifying individual Afghan women, as well as women's networks and organisations, I analyse their different coping strategies and the ways in which networking and different forms of group solidarity became mechanisms for women's empowerment. Third, I demonstrate how, throughout Taliban rule, many women risked their lives by turning their homes into underground networks of schools for girls and young women. I argue that, as social actors, they created cohesion and solidarity in their communities. Their secret organisations have already laid the foundation for the building of social capital, which is crucial for the process of reconstruction in Afghanistan. In the final section, I propose that women in Afghanistan, as social actors, are optimistic and willing to participate in the process of reconstruction. As a researcher, I intend to articulate their voice, views, and demands, which I hope will be taken into consideration by policy makers and aid workers.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".