Thinking globally, acting locally – conservation lessons from Oceania
Bibliographic record
Abstract
Oceania is a diverse region encompassing Australia, Melanesia, Micronesia, New Zealand and Polynesia, with six of the world's 39 hotspots of diversity but a poor record for extinctions from widespread threats to biodiversity. The region is also culturally diverse, containing close to a quarter of the world's languages and some of the oldest cultures. This makes the region a priority for immediate and sustained conservation action. In this special issue we provide local conservation solutions in Oceania to global problems, capturing the diversity of nations, cultures and environments. The issue is organised by the major threats faced in the region: habitat loss, over exploitation and invasive species. Case studies, framed as coupled problem-solutions, include examples from Australia, New Zealand and the Pacific and contrast findings across regions and realms. There are successes and failures faced by conservation in this local region, and the analysis within this special issue offers lessons for conservation globally.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".