Bibliographic record
Abstract
Before effective anti-poverty policy can be designed and implemented, the extent, trend and distribution of poverty must be identified. In this sense, poverty measurement is a crucial intermediate step in public policymaking and development planning. This paper asks whether the estimated proportion of the world’s population with income below US$1 (adjusted according to purchasing power parity) per day is a good measure of trends in global poverty. We argue that the answer depends on two important issues in the measurement of poverty—the definition of the poverty line, and how best to summarize the level of poverty In this paper, we survey the literature on poverty measurement, demonstrate the importance of considering poverty incidence, depth and inequality jointly, present a simple but powerful graphical representation of the Sen and SST indices of poverty intensity (the poverty box) which is the FGT index of order 1 and extend our empirical work to China using the commonly accepted international poverty line definition of one half median equivalent income. – development ; poverty ; measurement ; China ; rural ; urban
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.028 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".