Mapping recreational fishers’ informal learning of scientific information using a fuzzy cognitive mapping approach to mental modelling
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
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.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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