Tools for the implementation of integrated water resources management (IWRM) in the Caribbean
Bibliographic record
Abstract
While many countries and regional authorities in the Caribbean have embraced the concept of integrated water resources management (IWRM) and recognized its guiding principles as beneficial, few have possessed the capacity to implement it since its enunciation in the Dublin Principles of 1992. The Caribbean Water Initiative (CARIWIN) endeavoured over a 6-year period, 2006–2012, to build capacity in a collaborative process with national governments and regional and international agencies. The result of this collaborative process was the selection of three Caribbean-specific tools to support the implementation of the key components of IWRM. These tools were National Water Information Systems, the Caribbean Drought and Precipitation Monitoring Network, and Community Water Strategies. This paper describes these three tools and the process promoted through CARIWIN for their successful adoption and implementation, i.e. a program including professional development, institutional partnerships, research, and dissemination of knowledge.
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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 | 0.056 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| 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".