Bibliographic record
Abstract
Beyond academic circles, there has been an expanding interest in Trinidad and Tobago, on the level of poverty in the country. Newspaper columnists and many others have joined a debate that is, unfortunately, largely based on speculation. Poverty and its measurement have always been contentious issues, more so when political contestation is imported into the debate. There has been a recent report attributed to the UNDP, which suggests a rather high level of poverty for the country, based on the use of purchasing power parities - PPP. It is not clear to this author how the PPP was generated, nor by whom. Moreover, PPPs have a limited utility and although there has been substantial work done on improving it as a tool for cross-country comparisons, there still remain difficulties that suggest that care needs to be exercised in deriving too much from it. This short paper will look at some of the data that are available and will rely on one recent study that can claim to be anchored on firm statistics and methodology in casting some light on the matter. A working hypothesis is that whatever the estimate of poverty, it is the dynamics of poverty identified in data beyond the estimate, that provide better insights into the development issues faced in attacking poverty.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".