Reporting time period matters: quantifying catch rates and exploring recall bias from fisher interviews in Thailand
Bibliographic record
Abstract
Catch rates reported by fishers are commonly used to understand the status of a fishery, but the reliability of fisher-reported data is affected by how they recall such information. Recalling catch may be influenced by the choice of reporting time period. Using interview data from fishers in Thailand, we investigated (1) how the time period for which fishers report their catch rates (e.g., per day or month) correlates with annual catch estimates and (2) the potential of recall bias when fishers reported multiple catch rates. We found that the annual catch estimates of fishers who reported on a shorter time period (haul, day) were significantly higher than those reported on a longer time period (month, year). This trend held true when individual fishers reported over multiple time periods, suggesting recall bias. By comparing fisher reports with external data sets, we identified that the mean across all reports was most similar to other data sources, rather than any time period. Our research has strong implications in using fishers’ knowledge for fisheries management.
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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 | 0.022 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".