Shifting gears: Diversification, intensification, and effort increases in small-scale fisheries (1950-2010)
Bibliographic record
Abstract
Locally sustainable resource extraction activities, at times, transform into ecologically detrimental enterprises. Understanding such transitions is a primary challenge for conservation and management of many ecosystems. In marine systems, over-exploitation of small-scale fisheries creates problems such as reduced biodiversity and lower catches. However, long-term documentation of how governance and associated changes in fishing gears may have contributed to such declines is often lacking. Using fisher interviews, we characterized fishing gear dynamics over 60 years (1950-2010) in a coral reef ecosystem in the Philippines subject to changing fishing regulations. In aggregate fishers greatly diversified their use of fishing gears. However, most individual fishers used one or two gears at a time (mean number of fishing gears < 2 in all years). Individual fishing effort (days per year) was fairly steady over the study period, but cumulative fishing effort by all fishers increased 240%. In particular, we document large increases in total effort by fishers using nets and diving. Other fishing gears experienced less pronounced changes in total effort over time. Fishing intensified through escalating use of non-selective, active, and destructive fishing gears. We also found that policies promoting higher production over sustainability influenced the use of fishing gears, with changes in gear use persisting decades after those same policies were stopped. Our quantitative evidence shows dynamic changes in fishing gear use over time and indicates that gears used in contemporary small-scale fisheries impact oceans more than those used in earlier decades.
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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".