2. Lessons from Community-Based Resource Management 3. Migratory Marine Resources as a Special Challenge to Commons Theory
Bibliographic record
Abstract
I carried out my first study of community-based resource management in the mid-1970s in the Cree Indian village of Chisasibi, James Bay, in eastern subarctic Canada. As a recent science PhD, I had no training to appreciate local resource management institutions and traditional knowledge. Worse, as a member of a generation of students under the influence of the “tragedy of the commons ” concept, I was predisposed to believing that resources had to be protected from the users by government resource managers and appropriately trained scientists. This belief was shaken somewhat by the results of my studies of Cree fishers and their productive and orderly fishery [BERKES 1977]. This was a subsistence fishery, with no commercial component, carried out in the coastal waters of James Bay. There were no apparent rules or regulations in its conduct. As an indigenous subsistence fishery, it operated outside the sphere of government regulations. Yet, as it turned out, there was indeed a system, and the fishers were self-organized and self-managed, unlike the “tragedy of the commons ” [BERKES 1999, chapter 7, summarizes some ten years of work with this fishery]. The “tragedy of the commons ” is often a starting point in commons discussions. Until the 1980s, it was the principal way in which commons were considered. Hardin [1968] used the
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".