Caribbean Economics Quarterly: Volume 12, Issue 1: Reflections on Innovation and Productivity as Caribbean Businesses Emerge from the Pandemic
Bibliographic record
Abstract
This edition of the Caribbean Economics Quarterly focuses on the issue of long-term economic growth. As economies have recovered from the pandemic, the main question is: Will the region return to the slow long-run growth of the pre-pandemic period? The key lies in innovation and productivity. To understand the challenges facing businesses to innovate and increase productivity, the authors draw on recent firm-level data from the Compete Caribbean Partnership Facilitys Innovation, Firm Performance and Gender (IFPG) survey. This edition starts with an overview of past performance of Caribbean countries in terms of economic growth and productivity. It then describes the IFPG data and summarizes recent research papers analyzing those data and the conclusions emerging from that research. Finally, the country chapters draw on the Compete Caribbean database to describe the challenges facing firms at the country level.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".