Partnerships Generate Co-Benefits in Agricultural Stream Restoration (Canterbury, New Zealand)
Bibliographic record
Abstract
In Aotearoa New Zealand, agricultural land-use intensification and decline in freshwater ecosystem integrity pose complex challenges for science and society. Despite riparian management programmes across the country, there is frustration over a lack in widespread uptake, upfront financial costs, possible loss in income, obstructive legislation and delays in ecological recovery. Thus, social, economic and institutional barriers exist when implementing and assessing agricultural freshwater restoration. Partnerships are essential to overcome such barriers by identifying and promoting co-benefits that result in amplifying individual efforts among stakeholder groups into coordinated, large-scale change. Here, we describe how initial progress by a sole farming family at the Silverstream in the Canterbury region, South Island, New Zealand, was used as a catalyst for change by the Canterbury Waterway Rehabilitation Experiment, a university-led restoration research project. Partners included farmers, researchers, government, industry, treaty partners (Indigenous rights-holders) and practitioners. Local capacity and capability was strengthened with practitioner groups, schools and the wider community. With partnerships in place, co-benefits included lowered costs involved with large-scale actions (e.g., earth moving), reduced pressure on individual farmers to undertake large-scale change (e.g., increased participation and engagement), while also legitimising the social contracts for farmers, scientists, government and industry to engage in farming and freshwater management. We describe contributions and benefits generated from the project and describe iterative actions that together built trust, leveraged and aligned opportunities. These actions were scaled from a single farm to multiple catchments nationally.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".