No Mountain Too High? Assessing the Trans-territoriality of the Kailash Sacred Landscape Conservation Initiative
Bibliographic record
Abstract
Regional efforts to preserve mountain landscapes that account for half of the world’s biodiversity hotspots raise pertinent questions for existing statist discourses and practices of territoriality. The paper focuses on the Kailash Sacred Landscape Conservation and Development Initiative (KSLCDI), a transboundary Himalayan collaboration involving China, India and Nepal that seeks to conserve an area of shared cultural heritage and rich biodiversity. The UNEP-supported initiative, aimed at integrating regional, national and local actors redefines the role of the state from policy control to policy coordination. This prompts three key questions that the paper seeks to investigate. Firstly, how will states and sub-state actors negotiate divergent interests and approaches to natural resource management? Secondly, to what extent can spatiality be read with citizenship within the framework of transboundary conservation? Thirdly, what are the prospects for cross-border initiatives to reconcile conservation strategies devised at the national and regional levels with indigenous value systems, which have traditionally regulated local resource use? The paper is an enquiry into the Initiative’s potential to redefine the spatial and operational remits of state capacity and its implications for mountain governance.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".