Bibliographic record
Abstract
A major agenda of development studies is the persistent problem of poverty. While much has been said and done on poverty, this social disease continues to plague large sections of all societies, not least of all in the Caribbean region.This research is set in St. Lucia, a Caribbean island of 238 square miles. In order for significant strides to be made on the country's road to development, research must be done to inspire and inform policy-makers and development practitioners. At the time of the study there were mainly two (2) official documents on poverty in SI. Lucia. The most notable and widely used document that provides an understanding of national poverty in St. Lucia is the 1996 Poverty Assessment Report (PAR) commissioned by the Caribbean Development Bank (CDB). This report, which followed an extensive national survey, focus group discussions, interviews and community observations, has since been used, as a guide to policy formulation aimed at reducing poverty and enhancing the well being of communities in St. Lucia. While it is useful for the national, large scale data it provides there is need for more focused and specific investigation, to provide an even deeper understanding of poverty in St. Lucia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".