Linking Policies to Well-Being Outcomes Through Micro-Simulation
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.
Bibliographic record
Abstract
A major challenge in the measurement of well-being and progress is to link indicators of high-level societal outcomes with specific policy interventions. This is important not only for better informing the public, but also to provide the means for policy makers and advisors to assess the impacts of their policies and programmes and to increase their effectiveness and cost-efficiency. This paper looks at four major areas of social policies-health status, literacy and learning, economic security, and economic inequalitywith the aim of understanding how to link broad outcome measures of progress in these areas, on the one hand, and the policies bearing on them, on the other. Emphasis is given to the powerful benefits to be derived from coupling longitudinal, multivariate data and powerful statistical methods with recently developed analytical tools such as micro-simulation. The paper also emphasises the need for "principled" summary indicators, i.e. indicators embedded within coherent data systems, and the importance of internationally comparable data based on common concepts and definitions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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 it