Bibliographic record
Abstract
The academic literature contains numerous examples of the failures of both top-down and bottom-up common pool resource management frameworks. Many authors agree that management regimes instead need to utilize a multi-level governance approach to meet diverse objectives in management. However, many currently operating systems do not have that history. This paper explores the conversion of ancestral top-down regimes to complex systems involving multiple scales, levels and objectives through the management of the polar bear (Ursus maritimus) in its five range countries. The less successful polar bear management systems continue to struggle with the challenges of developing institutions with the capacity to learn and change, addressing multiple objectives while recognizing the conservation backbone to management, and matching the institutional scale with biophysical, economic and social scales. The comparatively successful institutions incorporate these features, but reveal on-going problems with vertical links that are partially dealt with through the creation of links to other groups.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".