From Community-Based Resource Management to Complex Systems: The Scale Issue and Marine Commons
Bibliographic record
Abstract
Most research in the area of common and common-pool resources in the past two or three decades sought the simplicity of community-based resource management cases to develop theory.This was done mainly because of the relative ease of observing processes of self-governance in simple cases, but it raises questions related to scale.To what extent can the findings of small-scale, community-based commons be scaled up to generalize about regional and global commons?Even though some of the principles from community-based studies are likely to be relevant across scale, new and different principles may also come into play at different levels.The study of cross-level institutions such as institutions of co-management, provides ways to approach scale-related questions and deal with linkages in complex adaptive systems.Looking beyond self-governance, community-based resource management needs to deal with multiple levels of governance and external drivers of change, as illustrated in this paper with examples of marine commons.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".