Suchetgarh Women: The Strength and Excluded Section of the International Border
Bibliographic record
Abstract
Jammu and Kashmir, a region straddling India and Pakistan, illustrates the intersection of two major South Asian states along the International Border (IB), where protracted violence profoundly impacts local communities. This study explores the lived experiences of borderland women in Suchetgarh village, Jammu, focusing on the impacts of ceasefire violations and hostile interactions between Indo–Pakistani forces. Utilizing a feminist methodology, the research highlights the profound effects of conflict on women's physical and psychological well-being. It underscores the dual victimization faced by these women: one stemming from entrenched patriarchal structures and the other from militarism and everyday violence. Despite enduring these adversities, borderland women contribute significantly to local economies, education, and social cohesion, embodying resilience and social unity. However, they remain marginalized in socio-political and security spheres due to prevailing patriarchal norms. The study advocates for increased female representation in security forces and policy-making to mitigate the adverse effects of militarized borders. It also emphasizes the potential for feminist perspectives to inform border security studies and improve women's roles in these regions. By focusing on borderland women's perspectives and their call for peace and dialogue, the study challenges traditional realist frameworks and offers insights into the human dimensions of Indo–Pakistan border conflicts. The research calls for enhanced understanding, empathy, and the incorporation of gendered voices to transform longstanding violent relations into peaceful conditions. Keywords: Borders, borderland women, Jammu and Kashmir, ceasefire violations, security
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".