Bibliographic record
Abstract
Abstract As a dominant paradigm, ecosystem‐based fisheries have to come to terms with uncertainty and complexity, an interdisciplinary visioning of management objectives, and putting humans back into the ecosystem. The goal of this article is to suggest that implementing ecosystem‐based management (EBM) has to be ‘revolutionary’ in the sense of going beyond conventional practices. It would require the use of multiple disciplines and multiple objectives, dealing with technically unresolvable management problems of complex adaptive systems and expanding scope from management to governance. Developing the governance toolbox would require expanding into new kinds of interaction unforeseen by the mid‐twentieth‐century fathers of fishery science – governance that may involve cooperative, multilevel management, partnerships, social learning and knowledge co‐production. In addition to incorporating relatively well‐known resilience, adaptive management and co‐management approaches, taking EBM to the next stage may include some of the following: conceptualizing EBM as a ‘wicked problem’; conceptualizing fisheries as social‐ecological systems; picking and choosing from an assortment of new governance approaches; and finding creative ways to handle complexity.
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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".