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Record W3124281625

Designing the Inequality-Adjusted Human Development Index (HDI)

2010· preprint· en· W3124281625 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2010
Typepreprint
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
FundersAustralian Agency for International DevelopmentInternational Development Research CentreGovernment of CanadaDepartment for International DevelopmentUnited States Agency for International Development
KeywordsHuman Development IndexInequalityIndex (typography)Human development (humanity)EconometricsMeasure (data warehouse)PopulationDistribution (mathematics)Income distributionComputer scienceEconomicsMathematicsData miningEconomic growthSociology
DOInot available

Abstract

fetched live from OpenAlex

<p>This paper proposes a method for adjusting the HDI to reflect the distribution of human development achievements across the population, and across dimensions, using an inequality measure from the Atkinson family. We begin with a discussion of the proposed indices in an idealized setting where variables and their scales have been identified and the data are available. We then address the practical issues that arise when applying these methods to real data. The final section presents and evaluates another related approach.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0000.001
Open science0.0060.007
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.087
GPT teacher head0.324
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it