Normative Choices and Tradeoffs when Measuring Poverty over Time
Bibliographic record
Abstract
<p>This paper examines the aggregation of an indicator of wellbeing over time and across people to measure poverty. We characterise the general form of an intertemporal poverty measure under mild normative principles and show that it must embody an unambiguous ordering of possible trajectories of an individual’s wellbeing. We motivate further normative principles and examine their consequences for the form of the measure, showing that some measures suggested in the literature are not consistent with these principles. We discuss additional stronger properties that may be argued to be desirable for an intertemporal or chronic poverty measure. We identify compatibilities and tradeoffs among certain of these properties. For example, a poverty measure cannot simultaneously capture chronicity of poverty and sensitivity to fluctuations. We argue that a poverty analyst should choose among these properties according to context and the particular conception of poverty she seeks to measure.</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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".