Community-based Approaches to Resource and Environmental Management
Bibliographic record
Abstract
(from the Introduction) Community-based approaches are advocated widely internationally and domestically, based on the idea that information, understanding and capacity for action and change, as well as for monitoring and enforcement, do not reside only within government agencies or the private sector. Certainly, these competencies and others often do exist within communities, outside of identified scientific, planning and management organizations. Recent commentators have noted, however, that much of what has been written takes it as self-evident that locally-based participatory approaches result in fairer and more equitable decisions, more potential for generation of context-appropriate innovations, more effective implementation of plans and implementation strategies and a generally more sustainable world. Unfortunately, reports from the field make it clear that this is not always the case (Cooke et al., 2001)... As one contribution to discussion of the problems and potential of community-based approaches to resource and environmental management, this theme issue of Environments has gathered together accounts of the experience of researchers working at the local level on three continents – Asia, South America and Europe. We view this issue as an opportunity to share what is being learned from research conducted in countries or regions outside of Canada, providing an opportunity to reflect on transferability of experience and lessons learned.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".