To EBFM or not to EBFM? that is not the question
Bibliographic record
Abstract
Abstract The ecosystem‐based fisheries management (EBFM) framework has a solid theoretical justification and has been embraced in principle by many regions; yet, systematic implementation remains a challenge. In regions with strong governance, single‐species stock assessment and management has been successful in ending overfishing and maintaining stocks near levels that produce maximum catches. However, considering species in isolation and recognizing a limited set of management objectives leads to systemic inefficiencies, incentivizes waste and generates unintended consequences. To avoid undesirable outcomes, human values and needs must be positioned at the forefront of management, system‐level objectives must be identified, and management actions must be systematically evaluated to ensure they are contributing to those larger objectives. Such processes, when implemented transparently, will lead to reduced conflict and improved stakeholder support for governance and should greatly facilitate long‐term management. We argue here that, regardless of the management framework adopted, we inherently manage at the ecosystem level—albeit sometimes “blindly”—and that increased attention to ecosystem objectives and trade‐offs will improve management outcomes.
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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".