Good Governance, the Informal Sector and Protection of Competition in the Market: Lessons from CARICOM
Bibliographic record
Abstract
Competition law protects the process of competition int he market by proscribing anti-competitive business conduct by public and private economic actors. The assumption is that enforcement of such rules would lead to welfare benefits for consumers and entrepreneurial opportunities for producers as firms become more efficient, offer better products at lower prices, and leave open opportunities for market entry by competitors. Yet, an examination of competition issues in selected CARlCOM countries revealed that, even if there were competition law and effective enforcement in these economies, this would have been insufficient to protect the process of competition in the market. Interviews in these economies revealed that intervention in the market by government officials to favour some competitors above others, and illegal activities in both the formal and informal sectors were distorting competition in the market. This points to the need to address issues of good governance simultaneously with introducing and implementing a competition regime.
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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) | 0.000 | 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 | 0.000 |
| 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".