Unraveling the Relationship between Collective Action and Social Learning: Evidence from Community Forest Management in Canada
Bibliographic record
Abstract
An important outcome of social learning in the context of natural resource management is the potential for collective action—actions taken by a group of people that are the result of finding shared or common interest. Evidence of the relationship between collective action and social learning is beginning to emerge in the natural resource management literature in areas such as community forestry and participatory irrigation, but empirical evidence is sparse. Using a qualitative inquiry and research design involving a case study of the Wet’zinkw’a Community Forest Corporation, this paper presents research that examined the relationships between collective action and social learning through community forest management. Our findings show strong evidence of collective action outcomes on the part of board members responsible for the community forest, such as establishing a legacy fund, adding value to logs, protecting First Nations cultural values, and hiring locally. Our data also reveal that the actions taken by board members were encouraged through social learning that was related to acquiring (new) knowledge, developing an improved/deeper understanding, and building relationships. However, we found limited opportunities for community forest partners and the general public to learn and contribute to collective action outcomes since the actions taken and associated learning occurred mainly among board members.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".