Dynamics of social–ecological changes in a lagoon fishery in southern Brazil
Bibliographic record
Abstract
Introduction Any resource management system has two interrelated dimensions: the social system and the ecological system. These dimensions are often treated separately. In the last decades, considering the failure of many conventional resource management systems (Ludwig, Hilborn, and Walters, 1993), some researchers have started investigating the dynamics of integrated social and ecological systems (henceforth social–ecological systems) in order to improve resource management (Gunderson, Holling, and Light, 1995; Berkes and Folke, 1998). To analyze the dynamics of social–ecological systems, we use common-property theory and adaptive management. The development of common-property theory (McCay and Acheson, 1987; Berkes, 1989; Ostrom, 1990; Bromley, 1992) has provided key tools for the understanding of the social dimension of management systems. A common-property (or common-pool) resource (defined as a class of resources for which exclusion is difficult and joint use involves subtractability) can be managed under four ‘pure’ property rights regimes: communal property (community-based management), state property, private property, or open access (lack of a property rights regime). In reality, many resources are managed under various mixes of these regimes, as in co-management characterized by a sharing of responsibility between the government and user groups for resource management. The degree of participation of government agencies and user groups in the decision-making process may vary greatly from one co-management case to another (McCay and Jentoft, 1996; Pomeroy and Berkes, 1997).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".