Glaciers’ contributions to people, nature’s values, and coping strategies in the Indian Himalaya
Bibliographic record
Abstract
High-altitude regions are highly vulnerable to impacts of climate change because the retreat of glaciers and snow impacts ecosystems, local livelihoods, cultural practices, and values. Although glacier change is widely documented within the natural sciences, limited focus has been given to the lived experiences of emerging challenges in changing glacier environments. We used a mixed methods approach to quantitatively and qualitatively explore and map glaciers’ contributions to people and identify patterns of change in the Indian Himalayan Regions of Ladakh and Uttarakhand, as well as local impacts and coping strategies. We found that glacier retreat undermines glaciers’ contributions to people, particularly for providing water, but also impacting culture, including spirituality and sense of place, and the environment, including local ecosystems, fauna, and flora. Glaciers were seen by local communities to sustain important instrumental (for food production), relational (for existence), and intrinsic (for biodiversity) values. The ability to diversify livelihoods and purchase power are perceived as important factors to cope with change, and subsistence farmers and herders are identified as vulnerable groups to glacier change. Our findings point to a conceptualization of glaciers not merely as physical entities and indicators of climate change, but also as indicators of the intricate and reciprocal relationships between people, nature, and culture. Operationalizing diverse values of nature in decision making requires acknowledgement of different needs, purposes, capacities, epistemologies, and knowledge systems of multiple actors. Examples show that there are discrepancies between how values of nature are currently framed and how they are manifested and experienced on the ground, which underscore the importance of adopting context-sensitive frameworks.
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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 | 0.002 | 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) | 0.001 | 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".