Steering governance through regime formation at the landscape scale: evaluating experiences in Canadian biosphere reserves
Bibliographic record
Abstract
Advocates of an ecosystem approach to establishing and managing protected areas recognize the complex dynamics between natural and social systems. This complexity includes the need for people to help restore and maintain ecological integrity and biological diversity while preserving a sustainable livelihood for themselves and for their communities (Slocombe, 2003; Dorcey, 2003; Ellsworth and Jones-Walters, 2006). This understanding is accompanied by a call to increase democratic processes for making decisions about the management of those areas, in particular to include local people in decisions that affect them directly (Cortner and Moote, 1999; Bagbey and Kusel, 2003). Community participation could range from education and stewardship projects to negotiated co-management agreements for governing natural resources, such as fisheries or forests. Francis (this volume) provides a more global overview of governance and systems perspectives that influence or impact upon protected areas. We portray some of the ways these larger-scale factors are exemplified more immediately within protected areas situated in regional landscapes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| 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".