Discount factors and the performance of alternative fisheries governance systems
Bibliographic record
Abstract
Abstract We investigate the performance of different governance arrangements (command‐and‐control, self‐governance and co‐management) in terms of sustainability and conservation when the discount factor of the regulator is different from the discount factor of fishers. For exogenous discount factors, self‐governance management regimes do better than command‐and‐control in terms of the long‐term sustainability of the fish resources, if the fisher’s discount factor is higher than that of the regulator, and vice versa if the discount factor of the regulator is higher. Under the assumption of endogenous discount factors, the decision whether to promote a command‐and‐control management system or a self‐governance or co‐management structure will depend on: (i) the magnitude of the intertemporal preferences of both the fishers and the regulator; and (ii) the relative weight or political influence of the fishers on the regulators’ decision‐making process. Hence, this contribution highlights the possibility that command‐and‐control can be less sustainable than self‐governance and vice versa. It is therefore important to explicitly take account of intertemporal preferences in the decision‐making process if a governance system for a given fishery is to succeed. For many fisheries, it is difficult to know the ‘true’ discount factors of both fishers and governments, hence, the practical message from this paper is that to guide against over exploitation of fishery resources, it is prudent to put in place co‐management arrangements, since both discount factors, whatever they may be, will be weighted into the decision‐making process.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".