Comparative aspects of mountain land resources management and sustainability: Case studies from India and Canada
Bibliographic record
Abstract
India and Canada share a common heritage in natural resources management. Both have a colonial background, settlers and indigenous peoples; there is a history of management agencies with utilitarian attitudes, and a history of treating public lands as commodities for commerce rather than as resources for local livelihoods. This historical context guided the overall goal of this study, which was policy development for the sustainable use of mountain environments. Interviews, workshops and seminars were held with local people and resource management professionals in a comparative case study in two regions; the Kullu area in Himachal Pradesh, India and the Arrow Lakes area in British Columbia, Canada. The paper is organized around two main objectives of the work relating to the successes and failures of mountain environment resource management policies and the development of criteria for assessing and monitoring sustainability in mountain environments, in particular, criteria for examining relevant crosscultural dimensions of sustainable development in these environments. By way of conclusion the paper considers further ways in which traditional resource policy development and implementation is being challenged by changing values and priorities; ecosystems management with people; and co-management and public participation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".