Review of factors influencing social learning within participatory environmental governance
Bibliographic record
Abstract
Participatory environmental governance might foster social learning, which could lead to the necessary process of social change toward sustainable development. However, current research is still largely inconclusive regarding how and under what conditions participatory environmental governance enhances social learning. Here, my aim is to improve the understanding of how participatory framework conditions influence social learning and to provide a reference point for future research. I conducted a narrative literature review, consolidating multifaceted empirical research to identify and discuss factors that explain social learning. The literature comprised 72 publications and resulted in 11 factors that are highly interconnected. These interconnections denote the causes of social learning. However, some factors such as the personal characteristics of participants have only been marginally investigated. In addition, although cognitive change is theoretically an essential element of social learning, it has rarely been investigated in the reviewed studies. Knowledge acquisition was assessed most often, but does not always lead to cognitive change. A research gap was identified between what is theoretically discussed as social learning processes and what is empirically analyzed. This review therefore presents the state of knowledge about how participatory environmental governance fosters social learning and suggests future research.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".