Gendered mobilities in the making: moving from a pedestrian to vehicular mobility landscape in Shimshal, Pakistan
Bibliographic record
Abstract
Although feminist geographers understand gender and mobility as mutually constitutive social processes, few studies explain how gender relations are constituted in particular mobility contexts, and how and why they shape mobility patterns in specific socio-spatial circumstances. We address these questions in an analysis of gendered mobilities in Shimshal, Pakistan, which until recently have taken shape in the context of a pedestrian mobility regime. The gender and mobility relationship has transformed as vehicular mobilities have replaced pedestrian mobilities with the construction of the Shimshal road. To demonstrate empirically the co-constitution of gender and mobility, we analyze aspects of socio-spatial context that have shaped gendered pedestrian mobilities, followed by those associated with the new vehicular mobility regime that are modifying gender relations in Shimshal. Shifting gender relations reshape corporeal mobility patterns. Road infrastructure has enhanced men’s and youth’s outbound travel as wage earners and students, respectively. These mobilities have reshaped women’s capacity to move, constraining their mobility beyond the village. As prosperity becomes contingent on outbound movement, men’s and youths’ social horizons and mobilities are expanding, while women’s compromised access to mobility as a social resource produces new mobility hierarchies and gendered exclusions.
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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 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".