Agri-environmental Resource Management by Large-scale Collective Action: Determining KEY Success Factors
Bibliographic record
Abstract
Purpose: Large-scale collective action is necessary when managing agricultural natural resources such as biodiversity and water quality. This paper determines the key factors to the success of such action. Design/Methodology/Approach: This paper analyses four large-scale collective actions used to manage agri-environmental resources in Canada and New Zealand. These case studies were selected based on an analytical framework that identifies the main types of collective actions. They were analysed after an extensive literature review, interviews with group participants, and the preparation and discussion of the background reports of each case.Findings: Three categories of factors are identified based on the stages of developing a large-scale collective action. The first stage identifies issues. This paper finds that the key actors (farmers and concerned others) within the relevant geographical and ecological boundaries must discuss and share information about resource issues in order to come to a common understanding. In the second stage, leadership and support from both governments and non-governmental groups are important to undertake large-scale collective action. The third stage manages collective action. Here, rules need to be adjusted to the local resource conditions and institutions.Practical Implications: This paper shows how these key factors could be incorporated into agri-environmental policies.Originality/Value: Previous studies of agri-environmental resource management have focused on individual actions by farmers, with little discussion on the importance of collective action. In particular, there has been little research on large-scale collective action in developed countries.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".