Bibliographic record
Abstract
1. COMMONS CONCEPT AND THEORY I carried out my first study ofcommunity-based resource management in the mid-1970s in the Cree Indian village of Chisasibi, James Bay, in eastern subarctic Canada. As a recent science Phl]), I had no training to appreciate local resource management institutions and traditional knowledge. Worse, as a member ofa generation ofstudents under the influence ofthe tragedy of the 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 fishgry [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 ofgovernment regulations. Yet, as it turned cyut, there was indeed a system, and the fishers were selforganized and selfimanaged, unlike the tragedy ofthe [BERKEs 1999, chapter 7, sumniarizes some ten years ofwork with this fishery]. The tragedy ofthe is often a starting point in commons discussions. Until the 1980s, it was the principal in which commons were considered. Hardin [1968] used the example of an imaginary pasture in Medieval England to which cattle herders have free and open access (i.e. a commons). Each herder receives a direct benefit (say +1) from adding one more. animal to graze in the pasture, whereas the costs of degrading the pasture are shared by all (a fraction of-1). Thus, each herder has the incentive to put as many cattle on the pasture as he can. Putting more animals on the pasture is the economically rational choice; yet everyone exercising their rational choice leads to the degradation ofthe pasture-hence the tragedy. The James Bay. Cree fishery did not fit this model at all. The fishers were able to decide among themselves on the rules of conduct of the fishery, and were able to persuade more or less everyone to fbllow those The rules were not written down, and the Cree themselves did not think ofthem as rules. It was simply the way things were done. This locally designed fishing system was quite different from biological management systems generally applicable
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".