Having it all: can fisheries buybacks achieve capacity, economic, ecological, and social objectives?
Bibliographic record
Abstract
The objective of this study is to assess the performance of fishery buybacks so as to determine the conditions under which positive socio-economic outcomes can occur during the process of fisheries adjustments. We do this by conducting a desk top review and supplementing the literature with targeted interviews with experts who have direct knowledge or experience with the implementation of buybacks. We focus on four case studies: Australia, the United States, British Columbia (Canada), and Norway. The outcome of each buyback was assessed in terms of the extent to which it achieved its capacity, economic, ecological, and social objectives. Our results indicate that buybacks can be successful in achieving specific programme objectives, such as reducing fishing capacity and increasing economic profits, at least in the short term. However, none of the buybacks evaluated were a resounding success due to the presence of latent permits or licences, effort creep, and continued reinvestment in the fishery. Enabling conditions for positive social outcomes included a strong economy, accountable leadership, and social assistance programmes tailored to local fishing communities. This study is useful in informing future buyback programmes’ design and implementation.
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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".