Comparative Analysis of Mountain Landuse Sustainability: Case Studies from India and Canada
Bibliographic record
Abstract
"Mountain people typically have lived on the economic margins of society, making a living as woodcutters, herders, gatherers, and small-scale agriculturalists. Yet, for many societies, mountains are at the center of the universe. A number of mountains in Asia, such as Mount Kailas in Tibet, take on the character of the sacred mountain 'which stands as a cosmic axis around which the universe is organized in Hindu and Buddhist cosmology' (Bernbaum 1996). In our Indian study area (Figure 1), the mountains around the source of the Beas River are of great cultural and historical significance, as a site and inspiration of the Great Indian epics. \n \n"This suggests that the study of sustainability requires a broad approach, taking into account social and cultural matters, as well as the ecological and economic. We started the project with a special interest in the management of forested mountain environments, and in the use of participatory or people-oriented approaches to resource management. We adopted a view of sustainable development which explicitly included three elements: (1) the environmental imperative of living within ecological means, (2) the economic imperative of meeting basic material needs, and (3) the social imperative of meeting basic human and cultural needs. Such an approach to sustainable development is concerned with much more than maximizing resource yields. It covers a broad range of environmental values as well as economic and social needs, and opens up the scope of decision-making not only to a wider range of natural and social sciences but also to a range of stakeholders' interests affected by resource management decisions. \n \n"Under the overall goal of studying policy development for the sustainable use of forested mountain ecosystems, the objectives of this study were four-fold. We deal with each in turn and expand on the policy implications. \n1) To develop integrated methodologies best suited for the comparative study of land resource management policies in forested mountain ecosystems; \n2) To study the successes and failures of mountain environment resource management policies and their social, economic, and historical context as revealed in case studies; \n3) To evaluate and develop criteria for assessing and monitoring sustainability in mountain environments and in particular, for examining relevant cross-cultural dimensions of SD in these ecosystems; and \n4) To communicate policy implications of the study to the appropriate agencies and people concerned with resource management and sustainable development, and to interact with policy-makers."
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".