Capacity Building – Preparing Caribbean Public Sector Unions for an Economic Environment in Transition
Bibliographic record
Abstract
This paper seeks to examine the renewal of the Caribbean Public Sector trade unions (CPSU), and make a number of recommendations as it regards the capacity building and institutional strengthening of these unions. The paper begins with an introduction that lays the foundation for the discussion of capacity building for CPSU. It continues with a socio-historical background to the current difficulties facing the CPSU, and continues with an examination of a number of regional concerns for the CPSU. Based on the Regional concerns identified and the fact that CPSU need to step out of their traditional role of representation and, in addition, become concerned with national issues, the paper concludes with a number of recommendations for the way forward for the CPSU.Cet article examine le renouveau des syndicats du secteur public des Caraïbes (SSPC) et établit un certain nombre de recommandations eu égard à leur capacité de consolidation institutionnelle. L’introduction expose les éléments de fonds qu’il s’agit d’avoir à l’esprit pour comprendre la capacité d’action des SSPC. S’en suivent une présentation des conditions socio-historiques qui expliquent leurs difficultés actuelles, puis un examen des questions régionales auxquelles ils font face. De là, et prenant parti que les SSPC doivent sortir de leur rôle traditionnel de représentation, y inclus une prise en compte des questions nationales, l’article conclut en identifiant des voies d’avenir.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".