How do environmental governance processes shape evaluation of outcomes by stakeholders? A causal pathways approach
Bibliographic record
Abstract
Multi-stakeholder environmental management and governance processes are essential to realize social and ecological outcomes. Participation, collaboration, and learning are emphasized in these processes; to gain insights into how they influence stakeholders' evaluations of outcomes in relation to management and governance interventions we use a path analysis approach to examine their relationships in individuals in four UNESCO Biosphere Reserves. We confirm a model showing that participation in more activities leads to greater ratings of process, and in turn, better evaluations of outcomes. We show the effects of participation in activities on evaluation of outcomes appear to be driven by learning more than collaboration. Original insights are offered as to how the evaluations of outcomes by stakeholders are shaped by their participation in activities and their experiences in management and governance processes. Understanding stakeholder perceptions about the processes in which they are involved and their evaluation of outcomes is imperative, and influences current and future levels of engagement. As such, the evaluation of outcomes themselves are an important tangible product from initiatives. Our research contributes to a future research agenda aimed at better understanding these pathways and their implications for engagement in stewardship and ultimately social and ecological outcomes, and to developing recommendations for practitioners engaged in environmental management and governance.
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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 | 0.043 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".