www.cepr.net Poverty Reduction in Venezuela: A Reality‐Based View
Bibliographic record
Abstract
Venezuela has seen a remarkable reduction in poverty since the first quarter of 2003. In the ensuing four years, from 2003 to 2007,1 the poverty rate was cut in half, from 54 percent of households to 27.5 percent. (See Table 1). Extreme poverty fell even more, by 70 percent – from 25.1 percent of households to 7.6 percent. These poverty rates measure only cash income; as will be discussed below, they do not include non-cash benefits to the poor such as access to health care or education. If Venezuela were almost any other country, such a large reduction of poverty in a relatively short time would be noticed as a significant achievement. However, since the Venezuelan government, and especially its president, Hugo Chavez Frias, are consistently disparaged in major media, government, and most policy and intellectual circles, this has not happened. Instead, the reduction in poverty was for quite some time denied. Until the Center for Economic and Policy Research published a paper correcting the record2 in May 2006, publications such as Foreign
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".