Bibliographic record
Abstract
Despite significant advances in understanding the biophysical and institutional causes of overfishing, we have yet to make progress in addressing the depletion of our global fisheries stock. Investigations of potential solutions tend to be too broad (mischaracterizing global fisheries as a singular commons problem to be addressed at the supranational level) or too narrow (focusing on improving management of small fisheries at the micro level). This article attempts to bridge the gap between our scientific understanding of our collective dilemmas and their pragmatic solutions. Building on insights from Nobel laureate Elinor Ostrom, we frame the depletion of global fisheries as a nested set of diverse and interconnected collective action problems organized at different horizontal and vertical levels, where decisions and actions of one jurisdictional unit reinforce and amplify problems (and solutions) for other units. We examine features of the global fisheries system, such as nonstationarity, nestedness, and prohibitive transaction costs. Then, we explore some potential solutions. The success of our conservation goals depends on our ability to craft institutional rules at the lower levels that are adaptive to local conditions, address incentive misalignment issues, and allow for the transfer of positive externalities to adjacent and higher levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".