Notes on the Quality of Life of Artisanal Small-Scale Fishermen along the Pacific Coast of Jalisco, México
Bibliographic record
Abstract
Sustainable fishing includes the socioeconomic status of fishers. We combined empirical quality of life (QOL) and subjective lived experiences methods to explore the social sustainability of artisanal fishers in five fishery collectives along the coast of Jalisco, Mexico, where the average daily income is slightly above the poverty level. The QOL scores were also related to annual catch and incomes within each collective. A QOL index is used in this study that combines importance and achievement ratings scores; the results are indicative of an acceptable QOL for fishermen. The concept of lived experiences, incorporating aspects of life relating to Mind, Body, Work and People was explored through interviews with 12 fishers. The QOL data revealed that family and friends are important indicators related to positive QOL reported by the sample, while economic indicators were not important. Although four of the five collectives perceived that the future looks worse than the present and past, there was limited correlation between catch or income and QOL. However, while the lived experiences exercise in part supported the QOL findings, in that People was the most important dimension for almost all of the fishers interviewed, negative economic gaps related to poor catches and incomes were prevalent in the Mind and Work dimensions. The findings suggest that to understand the socioeconomic component of sustainable fisheries, both of these approaches should be considered, as they can illuminate different aspects of fishers’ lives that need to be considered during the development of fisheries’ management policies.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".