Strategies For Sustainable Development: Experiences from the Pacific
Bibliographic record
Abstract
* Contents * 1. Sustainable Development and the Pacific Islands - John Overton * PART I: CONTEXTUAL ISSUES * 2. Resources and the Environment - John Overton and R R Thaman * 3. An Historical and Political Context - Richard Wartho * 4. Culture and Society - Regina Scheyvens * 5. Livelihoods - Nicholas Purdie * 6. Land Tenure and Atoll Society in Kiribati - Peter King * 7. Migration and the Cook Islands - Kirsti Hooker and Judith Varcoe * 8. Vanua: Land, People and Culture in Fiji - Kalaveti Batibasaqa * PART II: EFFECTS OF DEVELOPMENT * 9. Logging in Melanesia - Regina Scheyvens and Ross Cassells * 10. Mining in Papua New Guinea - Regina Scheyvens and Leonard Lagisa * 11. Marine Resources - Salome XXX * 12. Population and Urban Environments - Donovan Storey * 13. Commodity Production and Unsustainable Agriculture - John Overton, Warwick Murray and Imam Ali * PART III SUSTAINABLE ALTERNATIVES * 14. Nature Conservation (and Aid in Western Samoa) - Ned Hardie-Boyes * 15. Sustainable Agricultural Options - John Overton * 16. Sustainable Forestry Options - Regina Scheyvens and Ross Cassells * 17. Ecotourism - Regina Scheyvens * 18. Sustainable Urban Footprints - John Overton and Donovan Storey * PART IV CONCLUSIONS AND ISSUES * 19. Conclusions - John Overton and Regina Scheyvens
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".