Social dimensions in island restoration: some case studies from Aotearoa – New Zealand
Bibliographic record
Abstract
Islands have been a focus for biodiversity conservation in Aotearoa – New Zealand for more than 50 years. Recognition of the impacts of invasive predators, the significant outcomes that can be anticipated following their removal, and growing capacity to eradicate suites of pests from larger islands have underpinned this progress. Increasingly, attention is being directed at treating larger inhabited islands as well as mainland restoration sites where people live nearby and where the social dimensions become increasingly important. The case studies presented here illustrate changes to better acknowledge, consult and collaborate with tangata whenua (local Indigenous people). A focus on forging and maintaining relationships with other local stakeholders such as landowners and community groups is also illustrated. Other social dimensions such as political advocacy and securing institutional and financial support are also outlined in the case studies. We conclude that while much is being learned about opportunities to address social dimensions, those involved in promoting and implementing island restoration will need to remain flexible and apply locally nuanced approaches that reflect social as well as other circumstances at each site.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".