Focused Inequality and Wellbeing Measurement for Public Policy Initiatives: Equalizing Opportunity and Levelling Up, A Spanish Example
Bibliographic record
Abstract
Abstract Beyond just equalizing opportunities, ‘levelling up’, ‘inclusive growth’ and ‘no child left behind’ policy initiatives require inequality measurement from a different perspective than conventional measures provide. Whereas standard inequality measures quantify normalized aggregate distance from some centrality parameter or distribution, these imperatives demand equalization towards targets that are not necessarily a centrality parameter or distribution dependent upon the underlying egalitarian philosophy. Here, Inequality Modulated Success Indices are proposed in the face of Utilitarian, Prioritarian and Sufficientarian Imperatives. The techniques meet the challenges of both continuously measured and ordered categorical environments and are exemplified in a study of human capital acquisition in Spain over the 2009–2015 period. When such considerations are introduced in the final analysis, wellbeing improvement is no longer universally observed across the three imperatives. Whilst, under a First Order wellbeing indicator, Utilitarians and Egalitarians see an improvement in wellbeing whereas Prioritarians do not, under a second order indicator Utilitarians see an improvement whereas both Egalitarians and Prioritarians see a deterioration.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".