Pressures on Egyptian Red Sea fisheries from the artisan fishers’ perspective
Bibliographic record
Abstract
The Egyptian Red Sea (ERS) supports artisanal, commercial and recreational fisheries, managed using a summertime closure, not applied to recreational fishing. Stock status is little known and management options are severely limited. To inform future management, we report the results of a questionnaire survey of artisan fishers from four Red Sea ports, collecting basic socioeconomic and fisheries data: fleet characteristics, self-reported landings, plus attitudes towards current management. Relationships among catches, technical and social categories and categorised attitudes were analysed using ordinal and multinomial logistic regressions. Median income of ERS artisanal fishers was 54% of the mean income from work in rural Egypt in 2020. Almost all respondents reported declining catches (61% blamed “overfishing”) and most were pessimistic (43% expected to remain fishing in 10 years time). The seasonal closure was poorly supported by artisanal fishers: 75% claimed it contributed to declining resources. The timing of the closure, intended to protect spawning fish, aligned with published spawning seasons for some, but not all important species. 66% of fishers identified this mismatch as the reason for policy failure. High dissatisfaction rates risk non-compliance: e.g. 23% of those interviewed switched to ‘recreational’ fishing during the closed season. Most artisan fishers of the ERS are in a precarious economic position, facing a declining resource, under management that few support, with concerns about the long term future for their livelihood. We suggest transition to a participatory approach with data-driven co-management as a long term solution.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".