Towards Social Regionalism: The Case of CARICOM (Hacia El Regionalismo Social: El Caso De CARICOM)
Bibliographic record
Abstract
English Abstract: This article argues that social regionalism has the potential to question the asymmetric and unidimensional visions of globalization by providing credible and fluid alternatives that use as a starting point the set of decentralized, and even micro-States including their constituent members, as the actors of civil society. This article offers a look beyond the social dimensions of commerce to think about the inherent connections between the social and the economical, and thus at social regionalism. Throughout, the article draws on the experiences of Caribbean Community (CARICOM) to present a limited example of social regionalism in the making. Spanish Abstract: El presente articulo sostiene que el regionalismo social tiene el potencial de cuestionar las visiones asimetricas y unidimensionales de la globalizacion, al proveer de alternativas creibles y fluidas que utilizan como punto de partida al conjuntode Estados descentralizados, e incluso micro-Estados, incluyendo sus miembros integrantes, como los actores de la sociedad civil. Les ofrece el potencial de buscar mas alla de las dimensiones sociales del comercio para pensar las conexiones inherentes entre lo social y lo economico, y de esta manera el regionalismo social. El articulo consiste entonces en redireccionar la atencion a un ejemplo limitado de regionalismo social que se esta construyendo: CARICOM.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".