The Building Blocks of Successful Regional Integration: Lessons for the CSME from other regional integration schemes.
Bibliographic record
Abstract
The Building Blocks of Successful Regional Integration: Lessons for the CSME from other regional integration schemes. By Rachel Simms, LL.M, 2006 University of Toronto And Errol Simms, Senior Lecturer, Department of Management Studies, Faculty of Social Sciences, U.W.I., St. Augustine. Regional Trading Blocs (RTBs) have become quite ubiquitous, with approximately 200 of them presently operating in the world trading system. However, many of these RTBs are not successful in their goal of improving the economic development of their respective regions. Indeed, in the last three decades many RTB’s have failed and have been dismantled. This paper seeks to identify the socio-economic factors which are necessary for successful regional integration. These factors have been divided by some commentators into demand factors, such as the potential for economic gain for each member state and supply factors, such as the existence of commitment institutions, for example a regional Court. This paper seeks to identify and examine the demand and supply factors for successful regional integration as distilled from past and existing regional integration experiences both in Latin America and in Europe. The paper will then go on to apply this learning to the CSME, by firstly assessing whether the requisite demand and supply conditions are met by the institutional arrangements of the CSME, and by suggesting ways in which the CSME can adopt, create or enhance the demand and supply factors which are necessary for successful regional integration.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".