Building Climate Solutions Through Trustful, Ethical, and Localized Co-Development
Bibliographic record
Abstract
The Small Island Developing States (SIDSs) in the Latin American and Caribbean region remain among the most vulnerable to climate change, as increasingly frequent and severe disasters threaten infrastructure, human life, and progress toward the Sustainable Development Goals. Addressing these risks requires urgent regional and localized approaches grounded in coordinated climate risk assessment, anticipatory action, and Earth observation science-informed modeling with key support from a strong global community of practice. However, barriers remain to achieving local adaptation measures, including global action measures that conclude before local uptake of climate resilience practices are realized, reinforcing cycles of project impermanence. In this paper, we detail a Jamaica-focused case study that articulates such barriers impeding science and data-informed disaster risk reduction strategies, policies, and durable project implementation. The case study was a longitudinal co-development initiative led by a team of Jamaican and international interdisciplinary, cross-sector experts on climate-related disasters in SIDS. Using principles of co-design, discourse analysis, and systems thinking, the study underscores the need for a place-based framework that centers relevant sectors of society and often-marginalized voices as foundational to bottom-up climate resilience. The resulting Relationship and Place-Based Framework offers a model for localized climate science and technology development and ethical international collaboration for climate action that emphasizes local ownership and self-determination, as bottom-to-top feedback loops are key for managing multi-hazard dynamics and residual risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".