Mapping recreational fishers’ informal learning of scientific information using a fuzzy cognitive mapping approach to mental modelling
Bibliographic record
Abstract
Abstract Fisheries management benefits from improving the communication of scientific information to recreational fishers through improved compliance and greater contributions during consultation and engagement. This study uses fuzzy cognitive mapping to collect detailed, graphic information about recreational fishers’ mental models as a way to improve the way scientific information is communicated to them. Fishers were given three examples of scientific information to understand the affective, cognitive and conative reactions to different types of fisheries‐related information that they often encounter, and mental models were derived based on topics they found most and least interesting. This study identifies driving variables and constraints to fishers’ interest in taking up scientific information. The results suggest a message's clarity, perceived regular usefulness, good and bad emotion and investments in money and time influence fishers’ interest in taking up scientific information. Fishers’ initial levels of interest in a topic also significantly affect the complexity of thought processes leading to further interest in informal learning and the relative roles of the driving variables and constraints.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".