Beyond headcount : measures that reflect the breadth and components of child poverty: OPHI working paper no. 45
Bibliographic record
Abstract
<p>This paper presents a new approach to child poverty measurement that reflects the breadth and\ncomponents of child poverty. The Alkire and Foster method presented in this paper seeks to answer the\nquestion ‘who is poor’ by considering the intensity of each child’s poverty. Once children are identified\nas poor, the measures aggregate information on poor children’s deprivations in a way that can be broken\ndown to see where and how children are poor. The resulting measures go beyond the headcount by\ntaking into account the breadth, depth or severity of dimensions of child poverty. The paper illustrates\none way to apply this method to child poverty measurement, using Bangladeshi data from four rounds\nof the Demographic Health Survey covering the period 1997–2007. Results for Bangladesh show that\nthe AF adjusted headcount ratio adds value because it produces a different ranking than the simple\nheadcount, because it also reflects the simultaneous deprivations children experience (intensity). Given\nthis, we argue that child poverty should not be assessed only according to the incidence of poverty but\nalso by the intensity of deprivations that batter poor children’s lives at the same time. The Bangladesh\nexample is used to illustrate how to compute and interpret the child poverty figures, how the final\nmeasure can be broken down by groups and by dimensions in order to analyse child poverty, how to\ninterpret changes over time, and how to undertake robustness checks concerning the poverty cut-off.</p>
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".