Historical Perspectives and Recent Trends in the Coastal Mozambican Fishery
Bibliographic record
Abstract
Historical data describing changing social-ecological interactions in marine systems can help guide small-scale fisheries management efforts. Fisheries landings data are often the primary source for historical reconstructions of fisheries; however, we argue that reliance on data of a single type and/or from a single scale can lead to potentially misleading conclusions. For example, a narrow focus on aggregate landings statistics can mask processes and trends occurring at local scales, as well as the complex social changes that result from and precipitate marine ecosystem change. Moreover, in the case of many smallscale fisheries, landings statistics are often incomplete and/or inaccurate. We draw on case study research in Mozambique that combines national landings statistics and career history interviews with fish harvesters to generate a multi-scale historical reconstruction that describes social-ecological interactions within the coastal Mozambican fishery. At the national level, our analysis points toward trends of fishing intensification and decline in targeted species, and it highlights the significant impact of small-scale fisheries on marine stocks. At the local level, fishers are experiencing changes in fish abundance and distribution, as well as in their physical, social, and cultural environments, and have responded by increasing their fishing effort. We conclude with a discussion of the governance implications of our methodological approach and findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".