Bibliographic record
Abstract
Abstract The evidence we have on chronic poverty and the fortunes of the poorest people suggests that a significant proportion of the poor, between one-quarter and one-half, are chronically poor in low- and lower-middle-income countries. Using the limited available data covering the last 20 years, this paper examines whether those who were poor in the 1990s could plausibly still be poor today, despite international and national efforts to eradicate poverty. The data on poverty dynamics are restricted to only a few countries, so this paper also explores the changing fortunes of the poorest quintile of the population between the 1990s and the 2000s from 33 Demographic and Health Surveys, concluding that significantly greater benefits (and fewer losses) from development across a range of indicators have gone to the second and third quintiles. This evidence shows that the poorest quintile have indeed lost out: they have not seen the same total amount of benefits as accrued by other wealth groups. The poorest have also lost more land and marry earlier in relative terms. Policies to equalize the benefits of development are wide ranging and often context specific. Many of them are not amenable to international goals and targets and they require positive political change and supportive change in social values. The main action to achieve greater equality is at the national level, and national policy makers need better and especially longitudinal data and analysis, particularly on wages and urban populations, if policies for the poorest are to improve significantly. The post-2015 framework needs to emphasise support for positive actions at national level and be sparing about imposing international goals and targets. While the MDGs focus on critical areas of policy, which should not be lost sight of, the one new goal which could draw attention to the plight of the poorest would be about equality/inequality in its various forms. While the political feasibility of such a goal is in doubt, a second best solution would be to develop equity/equality indicators across any other goals and targets, and then pay a lot of attention to them post 2015.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".