Low Discounting Behavior among Small-Scale Fishers in Fiji and Sabah, Malaysia
Bibliographic record
Abstract
Understanding the socio-economic factors that are associated with fishers’ willingness to delay gratification may be useful for designing appropriate fisheries management and conservation policies. We aim to identify the predictors of low discounting behaviour among fishers, which is analogous to having a longer-term outlook. We base our empirical study on two small-scale tropical reef fisheries in Sabah, Malaysia, and Fiji. We use an experimental approach to identify fishers with low discount rates, and then use a logistic regression model to identify predictors of low discount rates. We find that 42% of the respondents have low discount rates, and that site and village level variables are significant predictors of low discount rates. Within Sabah and Fiji, boat ownership and relative catch differentiate low discounting from non-low discounting fishers, but these variables have contradictory effects in Sabah and Fiji. Overall, our results imply that a substantial proportion of reef fishers may be willing to engage in conservation initiatives; however, local socio-cultural, economic, and ecological conditions have to be considered first during the process of designing management interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".