No longer tracking greenery in high altitudes: Pastoral practices of Rupshu nomads and their implications for biodiversity conservation
Bibliographic record
Abstract
Nomadic pastoralism has thrived in Asia’s rangelands for several millennia by tracking seasonal changes in forage productivity and coping with a harsh climate. This pastoralist lifestyle, however, has come under intense transformations in recent decades due to socio-political and land use changes. One example is of the high-altitude trans-Himalayan rangelands of the Jammu and Kashmir State in northern India: major socio-political reorganisation over the last five decades has significantly impacted the traditional pasture use pattern and resources. We outline the organizational transformations and movement patterns of the Rupshu pastoralists who inhabit the region. We demonstrate the changes in terms of intensification of pasture use across the region as well as a social reorganisation due to accommodation of Tibetan refugees following the Sino-Indian war in 1961 to 1962. We focus in particular on the Tso Kar basin - an important socio-ecological system of livestock herding and biodiversity in the eastern Ladakh region. The post-war developmental policies of the government have contributed to these modifications in traditional pasture use and present a threat to the rangelands as well as to the local biodiversity. In the Tso Kar basin, the number of households and livestock has almost doubled while pasture area has declined by half. These changes have potentially negative consequences for the long-term resilience of nomadic pastoralism as well as for the survival of rare local wildlife. To increase the pastoralist standard of living, having fewer pastoralists may be the only solution, and alternative livelihood options may bring this about. Development programmes should concentrate on enhancing opportunities for herders so that there is a greater diversity of employment opportunities and potentially better chances for the persistence of biodiversity.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".