Bibliographic record
Abstract
The paper examines poverty in urban Ethiopia using household survey data for 1994 and 2000. Consumption poverty is found to be high, with an overall head count of 47 per cent, in 1994, and 40 per cent, in 2000. As monetary measures may not appropriately capture welfare in non-monetary dimensions of life, non-monetary indicators, such as, subjective welfare status, nutritional status of children and housing characteristics are also examined. The findings indicate that there is a significant association between consumption poverty and subjective welfare status, but a weak agreement in ranking of households. Non poor households, in terms of consumption, are found to enjoy better housing amenities. However, the association between consumption poverty and child nutritional status is not strong. Poverty dynamics is also analysed using transition matrices and multivariate regression techniques. It was found that over 58 per cent of panel households had experienced poverty at least once during the period. Of these, over half had been chronically poor. The poverty transition was also quite significant with over a quarter of households experiencing a change in their poverty status. The results also showed that households with higher dependency ratio and whose heads are self employed, casual workers, pensioners and unemployed have a lower probability of exiting poverty. Those that are educated and belong to major ethnic groups have a higher probability of exit. Similar factors are significant in affecting the probability of entry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".