Great expectations for collective management: the mismatch between supply and demand for catchment groups
Bibliographic record
Abstract
Globally, agri-environmental policies targeting individual farmers have made little progress on the problem of diffuse water pollution, leading to increased demand for collective approaches to manage cumulative effects. To understand the emerging supply of collective institutions to meet this demand, researchers have studied local initiatives in many countries. However, the challenges of crafting new collective institutions are still poorly understood. In Aotearoa New Zealand, many farmers have established catchment groups in response to regulation of farming practices and public concern about unhealthy waterways. These groups typically do not have the features commonly expected of collective management institutions, e.g., few have specific environmental objectives or agreed actions or practices to protect resource sustainability. Our research with catchment group leaders, Indigenous representatives, and policy actors revealed differences in their logics about and expectations of catchment groups. These differences have given rise to a mismatch between the type of collective action that is in demand by government and the type being supplied by catchment groups. To bridge the supply-demand gap, agencies should seek to better understand their own logics while acknowledging the importance of groups’ priorities, and support groups to articulate goals and strategies and how these relate to government objectives. Conversely, catchment groups can be equipped with tools and insights to help them better understand their own motivations and goals, and those of agencies and other actors, to help them navigate these complex ideas and relationships in challenging and changing environments. In settler-colonial landscapes, resources should also be provided for Indigenous groups to realize their own aspirations and to bring their genealogical narratives to these conversations. More generally, it is important for agencies and other observers to understand what motivates collective entities, rather than assume that they share the management logic that informs collective management as described in the literature.
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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".