Multidimensional Poverty Measurement and Analysis: Chapter 4 - Counting Approaches: Definitions, Origins, and Implementations
Bibliographic record
Abstract
The measurement of poverty involves identification: the fundamental step of deciding who is to be considered poor. A 'counting approach' is one way to identify the poor in multidimensional poverty measurement, which entails the intuitive procedure of counting the number of dimensions in which people suffer deprivation. Atkinson (2003) advised an engagement between multidimensional measures from social welfare and the counting approaches due to the widespread policy use of the latter. This chapter reviews applications of the counting methods in the history of poverty measurement. We focus on empirical studies since the late '70s which developed relatively independently of each other in two regions. In Latin America, applications of the Unsatisfied Basic Needs Approach were widespread, often using census and survey data. European work drew on concepts of social exclusion and inclusion, and now include national and European initiatives.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".