Settings weights in multidimensional indices of well-being and deprivation: OPHI working paper no. 18
Bibliographic record
Abstract
Multidimensional indices of well-being and deprivation have become increasingly popular, both in the\ntheoretical and in the policy-oriented literature. By now, there is a wide range of methods to construct\nmultidimensional well-being indices, differing in the way they transform, aggregate and weight the\nrelevant dimensions. We present a unifying framework that allows us to compare the different\napproaches and to analyze the specific role of the dimension weights in each of them. Through\ninteraction with choices about the transformation and aggregation of the different attributes, the weights\nplay a crucial role in determining the trade-offs between the dimensions. Setting weights thus reflects\nimportant value judgements about the exact notion of well-being. We survey six methods to set weights\nusually employed in the literature. Three principles guides our assessments: first, weights should be made\nexplicit and clear so that they can be subject to public scrutiny; second, weights should be set taking into\nconsideration their role determining the trade-offs between dimensions; finally, weights should respect\npeople’s preferences about these dimensions.
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".