Measuring behavioral social learning in a conservation context: Chilean fishing communities
Bibliographic record
Abstract
Abstract In the sustainability and conservation sciences, “social learning” is defined as a group process which depends on trust and social capital and tends to boost conservation outcomes. We term this “collaborative social learning.” Meanwhile, the behavioral sciences define social learning as the individual use of socially acquired information and seek to explain how individuals employ social learning as part of adaptive behavior. We term this “behavioral social learning.” However, the influence of behavioral social learning on ecological outcomes is poorly understood. We conducted a study of behavioral social learning among fishers in seven communities in Chile's Region V to probe its connections with ecological outcomes and collaborative social learning. We develop and employ a novel behavioral measure of individual social learning in a simple fishing game in which fishers may pay a portion of their game earnings to observe and learn from other fishers in the game. We explore the internal and external validity of the instrument. The self‐consistency of game play, learning, and participant reflections reveals strong internal validity of the learning game. Additionally, game behavior is correlated with factors such as migration history, and the perceived availability of peers from whom to learn, suggesting the method also holds external validity. We then test whether factors associated with collaborative social learning, such as social capital, are related to social learning behavior as measured by the experiment. Interestingly, many correlates of ‘collaborative social learning’ are not strongly correlated with ‘behavioral social learning’ in our sample. We argue that this disconnect can help improve our understanding of the emergence of community‐based conservation and positive ecological outcomes as well as ‘collaborative social learning’ itself. Finally, we provide guidance on how behavioral measures of social learning could benefit community‐based natural resource management and conservation.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".