Caribbean Women in Globalization and Economic Restructuring
Bibliographic record
Abstract
A few months ago we attended a meeting in Caribbean island of Trinidad. We were there on behalf of University of Regina Canada to initiate a project in partnership with University of West Indies St. Augustine Campus. Tired of standard western fare offered by hotel in which we were staying we set out one afternoon to sample some of local cuisine. Our search took us to a local eating establishment that was well recommended for its authentic foods and extremely generous prices. While food service and price surpassed our expectations it was organization of this informal eatery that drew most interest. The eatery was located under a bright yellow zinc shed comprised of several tiny makeshift kitchens that were run by women. While women cooked cleaned served and collected money their young daughters peeled plantains and filleted fish for next days meals. Many of these women confided that they were once teachers and government workers but had resorted to this type of business in order to make ends meet. One commented that the teaching job doesnt pay anymore ... not since IMF while another explained sitting at a desk doesnt give a big pay cheque. It cant fill belly. (excerpt)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.019 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".