Collaborative agri-environmental governance in the Netherlands: a novel institutional arrangement to bridge social-ecological dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The theoretical benefits of collaborative landscape-scale approaches to agri-environmental land management have been widely discussed. However, there is little empirical study of the practical governance mechanisms through which such collaborative management may be realized. In 2016, an innovative collaborative agri-environmental scheme was established in the Netherlands. In this scheme, “agricultural collectives”—i.e., groups of farmers organized as certified conservation organizations—are collectively responsible for the implementation of agri-environmental policies at the local level. With a focus on the Dutch model’s multi-level governance dimensions, this article examines how devolving important aspects of decision making on agri-environmental management to the level of a collective body of farmers shapes the implementation of agri-environmental policies on the ground. Based on new empirical data, we highlight the important roles of agricultural collectives in balancing trade-offs between ecological and social targets when setting environmental objectives, coordinating landscape-scale management, and contracting individual farmers. At the same time, the local embeddedness of agricultural collectives and close interpersonal ties can give rise to new governance risks that need to be considered, including goal divergence between the collectives and public bodies, as well as cases of prioritization of social interests over ecological interests in agri-environmental management. As we argue, combining governance through agricultural collectives with a high level of transparency regarding contracting decisions, as well as enhancing the inclusivity of the scheme through new funding opportunities for agri-environmental management, can optimize the benefits of these collectives.
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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 | 0.000 | 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) | 0.001 | 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 it