Gendering Bordered Exclusion: Gender Politics and Everyday Spatialization in Indo-Pak Border Infrastructure
Bibliographic record
Abstract
The study explores the transformation of Punjab into a borderland after India’s Partition and examines the effects of infrastructural barriers on everyday lives of villagers, particularly women. An ethnographic study was conducted on two border villages – Audar and Mulakot, and Schindler’s (2015. Architectural Exclusion: Discrimination and Segregation Through Physical Design of the Built Environment. The Yale Law Journal 124, no. 6: 1934–2024) theory of architectural exclusion was employed to analyze the findings. Our study finds that everyday spatialization reflects the patriarchal norms of the villages and the security anxieties of the state. The infrastructural edifice is planned in a manner that they create a palisade in the name of protecting women. Such a design mainstreams tight control in a layered manner, leading to concentric circles of disadvantage where the immediate dominance is wielded by the men in the family, and the second layer of direct control is exercised by the armed forces. To conclude, the creation of bounded borderland spaces with restrictive infrastructure leads to women’s absence from public spaces.
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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 | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".