Growing Into Poverty: Reconstructing Peruvian Small-Scale Fishing Effort Between 1950 and 2018
Bibliographic record
Abstract
Small-scale fisheries are globally marginalized by management institutions; thus, they have to endure the consequences of ineffective regulations, environmental uncertainty, social traps and market inequity. Small-scale fisheries in Peru, one of the world's leading fishing countries, are important contributors to national employment, food security and gross domestic product. Yet, relatively little is known about these fisheries and their evolution, except for the fact that the Peruvian small-scale fleet size is rapidly increasing. Here, we developed several indicators to assess changes in the fishing efficiency and economic performance of this fleet. Segmented regression analysis was used to identify statistically significant breakpoints and changes in their trajectories between 1950–2018. Our results suggest that fishing effort has strongly increased, and at much faster rates than the catches, particularly since 2006. The combined effect of these trends results in significant declines in the fleet's ratio indicators (i.e., catch per unit of effort, revenue per unit of effort, and fisher's incomes relative to Peru's minimum wage), suggesting that the growing fishing effort is unsustainable and uneconomic. The behavior of these indicators differs within the fleet, depending on the vessel's main fishing method. Most small-scale fishers are currently living in relative poverty. Yet, fishers using the least selective fishing gears, or engaged in illegal fishing, had the most stable incomes over the past decade. These findings are discussed in detail by exploring the social, legal and economic drivers fostering fleet growth. Finally, a list of general recommendations aimed at improving fisheries sustainability and fisher's wellbeing was produced, based on the local context, fisheries literature and common sense.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".