Whose Governance? IMF Austerities in a Small Island State: The Case of Jamaica
Bibliographic record
Abstract
The International Monetary Fund and the World Bank have for a long time embarked on what can be described as a 'trustee' relationship with countries in the Commonwealth Caribbean. From the latter half of the 1970s, countries such as Trinidad and Tobago, Guyana, Barbados as well as Grenada were 'forced' because of their chronic need for 'hard' currency loans to approach the IMF and the World Bank. These loans were accompanied by structural adjustment measures. This paper attempts, for the first time, to evaluate, in the case of Jamaica, whether the measures introduced by the Lending Agencies resulted in some measure of economic growth in the countries under review. The paper then examines the new agreements entered into by these countries and the measures that accompanied them. The overarching argument is that the forces of globalization as well as austerity measures introduced by lending agencies such the IMF and the World Bank prevents rather than encourages small island governments1 to embark on 'national' development plans and programs. In other words, the primary argument of this paper is that these countries are constrained in their ability to 'govern' themselves; rather their economic decisions are largely crafted by the forces of globalization and further reinforced by international lending agencies such as the World Bank and the International Monetary Fund.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".