Future of Food and Agriculture in the Caribbean in the Context of Climate Change and Globalization: Where Do We Go from Here?
Bibliographic record
Abstract
In this chapter, we seek to synthesize the key themes, findings, lessons, and implications raised in the preceding chapters. Our aim is to make some generalizations about the interface between globalization, climate change, and agriculture and food in the Caribbean. We will focus on the lessons learned from the research and suggest some critical steps the Caribbean region might consider in addressing the dual threat of globalization and climate change—double exposure. We will consider where the region stands in its response to globalization and climate change. For example, are there opportunities we are missing in the banana, coffee, and sugar industries? Globalization is often discussed in terms of the inequities and how it disadvantages developing countries vis-à-vis the developed countries. But it is now clear that globalization presents opportunities if countries position themselves to take advantage of these. Based on the insights provided by the respective authors, and a broader analysis of the extant literature, we discuss ideas to reduce the adverse impact of globalization and climate change on the agricultural sector in the region.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".