Snow Leopards, Checkpoints, and Roads: Negotiating Selective Legibility in Hemis National Park, India
Bibliographic record
Abstract
Established with the goal of protecting endangered species, Hemis National Park in Ladakh, India, is a hotspot for trekkers and wildlife tourism. A process of selective legibility operates inside the park, as the conditions that make wildlife legible to the state are effectively rendering residents illegible because the building of infrastructure inside the park is either denied or delayed. Today, residents’ subsistence is largely predicated on the conditions of illegibility, as tourism-related incomes have displaced traditional agro-pastoral activities. Residents, however, have strong aspirations for roads, the absence of which complicates life within the national park. As the developing road network threatens to disturb residents’ subsistence, they developed a checkpoint, a device that recreates conditions of illegibility by preventing vehicle traffic inside the park. Ultimately, this article calls for attention to the creative ways communities negotiate their relationship with the state when facing a governance over space that leaves them marginalised.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".