MEASURING THE ATTRIBUTES OF POVERTY AND ITS PERSISTENCE: A CASE STUDY OF ERITREA
Bibliographic record
Abstract
This paper tries to identify the correlates of poverty in urban Eritrea using an estimation technique (the DOGEV model) that also allows for the inclusion of a measure of “persistence” in poverty levels from cross‐sectional estimation. The results suggest that 17 percent of the probability of being moderately poor and 22 percent of the probability of being extremely poor in Eritrea was attributable to this “persistence”—a predisposition toward poverty likely due to latent attributes related to past experience of poverty itself. The results also suggest that, in the post‐war economy of the mid‐1990s, those with vocational training fared best among all education groups. Being a war veteran also had a strong negative association with the poverty—reflecting successful attempts to support that group. The receipt of remittances also reduced the likelihood of poverty; though receipts from outside Eritrea had a much stronger effect than receipts from within Eritrea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".