Designing the Inequality-Adjusted Human Development Index (HDI)
Bibliographic record
Abstract
As a measure of wellbeing, national income misses variations in the things income can and cannot buy. It also misses variations in people’s claim on that aggregate income. The Human Development Index attempts to address the first weakness by incorporating two additional dimensions, health and education, into its informational bases. However, the second weakness, inequality, is ignored by the traditional HDI. In practical terms this means that any two countries having the same mean achievements will have the same HDI values even if they have very different distributions of achievements. This calls into question the accuracy of the HDI as a reflection of people’s actual achievements. This paper proposes a method for adjusting the HDI to reflect the distribution of human development achievements across the population, and across dimensions. 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 must be addressed when applying these methods to real data. The final section presents and evaluates another related approach.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".