Pacific connections for health, ecosystems and society: new approaches to the land-water-health nexus
Bibliographic record
Abstract
Renewed effort to understand the social-ecological context of health is drawing attention to the dynamics of land and water resources and their combined influence on the determinants of health. A new area of research, education and policy is emerging that focuses on the land-water-health nexus: this orientation is applicable from small wetlands through to large-scale watersheds or river basins, and draws attention to the benefits of combined land and water governance, as well as the interrelated implications for health, ecological and societal concerns. Informed by research precedents, imperatives and collaborations emerging in Canada and parts of Oceania, this review profiles three integrative, applied approaches that are bringing attention to the importance the land-water-health nexus within the Pacific Basin: wetlands and watersheds as intersectoral settings to address land-water-health dynamics; tools to integrate health, ecological and societal dynamics at the land-water-health nexus; and indigenous leadership that is linking health and well-being with land and water governance. Emphasis is given to key characteristics of a new generation of inquiry and action at the land-water-health nexus, as well as capacity-building, practice and policy opportunities to address converging environmental, social and health objectives linked to the management and governance of land and water resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".