Finding Emotions in the Drama of the Commons: A Multi-Relational and Multi-level Analysis of the Access to Fishery Resources in the Loreto Bay Marine Park, Baja California Sur, Mexico
Bibliographic record
Abstract
"The sad ending of Hardin's 'Tragedy of the Commons' has now been supplemented with a happy 'Comedy of the Commons.' Such 'balance' has kept intact the 'cold headed' rational individual responding to economic incentives mediated by the presence or lack of institutions. Drawing on research in the Loreto National Marine Park, I examine the role of different emotional relations in the cooperative behaviour for accessing fishery resources at the community, municipal and state levels. Results indicate that cooperative behaviour for accessing fisheries resources is strongly embedded in affective relations and widespread even under a de facto open access. Moreover, such emotionally engaged cooperation transcends individual attributes of occupation, locality and organizational levels, with important insights into the issues of resource users' heterogeneity and scale in the management of the commons. More generally, results support the thesis that emotions and reason are mutually complementary rather than exclusive, particularly when it comes to social facts such as human cooperation where positive emotions may be an essential element. If emotional bonds are a key force in cooperative behaviour, we should reconsider our theoretical stands and analysis regarding human cooperation, and how we go about promoting cooperative solutions for conservation of the commons through sustainable resource use."
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".