Bibliographic record
Abstract
This chapter presents Sen’s capability approach as a framework for well-being measurement with powerful and ongoing relevance to current work on measuring well-being in order to guide public policy. It discusses how preferences and values inform the relative weights across capabilities, then draws readers’ attention to measurement properties of multidimensional measures that have proven to be policy-relevant in poverty reduction. It presents a dual-cutoff counting methodology that satisfies these principles and outlines the assumptions that must be fulfilled in order to interpret ensuing indices as measuring capability poverty. It then discusses Bhutan’s innovative extension of this methodology in the Gross National Happiness Index and reflects upon whether it might be suited to other contexts. It closes with some remarks on relevant material in other Handbook chapters.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".