Trends, current understanding and future research priorities for artisanal coral reef fisheries research
Bibliographic record
Abstract
Abstract Artisanal coral reef fisheries provide food and employment to hundreds of millions of people in developing countries, making their sustainability a high priority. However, many of these fisheries are degraded and not yielding their maximum socioeconomic returns. We present a literature review that evaluates foci and trends in research effort on coral reef fisheries. We describe the types of data and categories of management recommendations presented in the 464 peer‐reviewed articles returned. Identified trends include a decline in articles reporting time‐series data, fish catch biomass and catch‐per‐unit effort, and an increase in articles containing bycatch and stakeholder interview data. Management implications were discussed in 80% of articles, with increasing frequency over time, but only 22% of articles made management recommendations based on the research presented in the article, as opposed to more general recommendations. Key future research priorities, which we deem underrepresented in the literature at present, are: (i) effectiveness of management approaches, (ii) ecological thresholds, trade‐offs and sustainable levels of extraction, (iii) effects of climate change, (iv) food security, (v) the role of aquaculture, (vi) access to and control of fishery resources, (vii) relationships between economic development and fishery exploitation, (viii) alternative livelihoods and (ix) integration of ecological and socioeconomic research.
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 | 0.053 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".