Sustainability science and education in Haiti and Puerto Rico
Bibliographic record
Abstract
This paper reports on the results of a workshop in Haiti and Puerto Rico to capture what priorities may be important to build sustainability sciences and education. In 2015, approximately 35 individuals attended all or part of the workshop at each location. Participants included academic leaders, university faculty, secondary school teachers, representatives of non-profit organizations, and university and high school students. Haitian participants called attention to the need for reforestation training, systemic solutions for waste management, and sustainable marine resources management. In Puerto Rico, participants called for more training to link civic engagement with sustainable development, social determinants of health, and programming on tsunami preparation and recovery. Members of both workshops asked for sustainability science and education advances in renewable and alternative energy development, general disaster and climate change impact preparedness (e.g. drought), and sustainable agriculture. Haitian and Puerto Rican participants also shared the view that engaging sustainability requires higher educational institutions to partner with communities, primary and secondary school teachers, policy-makers, and especially young persons, to reinforce the values of sustainability, and collectively work across sectors to learn through trial and error together.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".