Key principles of ecosystem‐based management: the fishermen's perspective
Bibliographic record
Abstract
Abstract Despite the growing popularity of ecosystem‐based management ( EBM ) in national legislation and in research and institutional literature, there is often an implementation gap ‘on the ground’, impeding widespread adoption in fisheries. This gap reflects in part the differing understandings of EBM held by fishermen and by management institutions. To explore and seek to close this gap, the underlying principles of EBM considered priorities by fishermen were systematically compared with the priorities identified in the published literature. The fishermen's priorities were determined by asking Atlantic Canadian fishermen to identify the EBM principles they consider most important. Four priority principles were identified: Sustainability, Stakeholder Involvement, Develop Long‐Term Objectives and Use of All Forms of Knowledge . The latter two were not frequently noted as priorities in the literature, while some literature priorities were less commonly chosen by fishermen, indicating a significant difference in perspectives on EBM . The rationale for fishermen's choice of priorities was explored by analysing the fishery management issues they raised – many directly connected to the above four priorities. In addition, another principle, Commit to Principles of Equity , often arose as an implicit priority among fishermen. We suggest that success in implementation of EBM may depend on reconciling differing priorities among its underlying principles, and combining knowledge and expertise from fishermen with research and institutional sources. The comparative methodology used here, which could be replicated elsewhere, should lead to better recognition of local challenges in EBM implementation and encourage support for EBM , to further its contribution to sustainable fisheries.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".