Working together to scale ecosystem restoration: collective approaches to community action in Aotearoa New Zealand
Bibliographic record
Abstract
Community groups are key drivers of ecosystem restoration in many countries. However, there is increasingly recognition that small scale restoration efforts are often insufficient to reverse ongoing biodiversity declines, and questions have been raised regarding the sustainability and efficiency of community-based initiatives. In Aotearoa New Zealand, collectives that bring together multiple community groups and other actors have arisen as a mechanism to scale restoration activities and support community restoration efforts. This article examines the nature, role, and contribution of ecosystem restoration collectives in Aotearoa, based on a survey of 27 collectives in 2021. Collectives generally engage in governance activities like funding, administration, and advocacy, adding to the typically “hands on” work of community groups. Similarly, they improve ways of working by increasing connections and communication between groups, agencies, and the wider public. This study indicates that collectives contribute to scaling restoration by improving the efficiency and sustainability of community initiatives, increasing the spatial scale and social-ecological scope of restoration, and increasing the range of actors involved in restoration.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".