Shifting the Dial: From wellbeing measures to policy practice
Bibliographic record
Abstract
At a time of economic turmoil it is perhaps unsurprising that the minds of policy makers focus on the question of how to restart economic growth. But in recent decades people have begun to question the adequacy of GDP as the primary indicator of the progress of societies. A number of governments, local, devolved and national have begun to explore how to measure wellbeing as a complement to traditional measures such as GDP. The project was carried out in partnership with IPPR North and provides evidence from six case studies of experiences of measuring wellbeing in France, the USA and Canada. The report concludes that wellbeing measures are at their most effective when they are supported by a combination of strong leadership, technocractic policy processes and building momentum through wide buy-in from civil society, citizens and the media. Where these elements come together, we have seen benefits for individual and community wellbeing by identifying policy gaps and innovative ways of working. It can also provide a valuable tool for holding governments to account.
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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 | 0.121 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.010 | 0.093 |
| Scholarly communication | 0.036 | 0.069 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.011 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".