Bibliographic record
Abstract
This paper focusses on the theoretical and computational framework in order to estimate the impact of economic growth or that of the change in inequality on poverty. During the last few years, there was a growing interest to perform such estimations and to anticipate the implication of some strategic policies, that can be adopted to meet the Millennium Development Goal (MDG, henceforth), that is to cut poverty by half. As is illustrated in this paper, estimated poverty changes may be less precise or even wrong. Precisely, this bad estimation occurs when the distributive changes are non-marginal, whereas the used approach is based on the assumption of marginal changes. In an other case, and where the estimation is implicitly based on a parameterized model of the income distribution, results may be less precise when the predicted distribution cannot reproduce perfectly that derived with the sample. In this study, by using some popular methods, we have used some household surveys of the African countries, as well as, fictive data to show the error size that can occur. Further, we propose a new numerical method to allow to estimate accurately the impact of distributive changes on 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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".