Short-term decisions of small-scale fishers selecting alternative target species: a choice model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The complexity of small-scale fisheries makes it difficult to predict the allocation of fishing effort among alternative target species in mixed fisheries, resulting in limitation for fisheries management. One reason for the difficulty is that fishing effort has been assumed as an aggregate of different components, without consideration of fishers' decisions. In this paper, we use discrete choice models to identify factors involved in fishers' decisions about selecting target species on a daily basis. We analyze catch data, by species and fisher, from three fishing communities of Yucatan, Mexico, to contrast the following models: (i) random selection, (ii) economic motivation, and (iii) changes in resource availability. Our results show that fishers do not operate at random but consider information on resource availability and revenues generated from previous trips before selecting or shifting a target. We compare the results among communities and also use the proposed models to predict changes in fishing effort levels given changes in species price and catch per unit effort. We stress the importance of understanding fishers' behavior when it comes to developing appropriate management policies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it