Policy Design for a Wellbeing Economy—Lessons from Four City Pilots
Bibliographic record
Abstract
Interest is growing in the idea of Wellbeing Economies—economic systems designed with the wellbeing of people and planet as a starting point. Governments around the world are increasingly curious about new measures of success that go beyond GDP and about transformative processes to help get there. The Wellbeing Economy Alliance (WEAll) was established in 2018 to accelerate the shift to wellbeing economies, and to amplify and support the growing movement helping to make it happen. This includes the Wellbeing Economy Governments partnership, which comprises New Zealand, Iceland, Finland, Scotland, and Wales, a group of small nations demonstrating leadership and practising collaboration as they strive to prioritise wellbeing in economic decision-making. As a growing movement, the Wellbeing Economy has a lot to offer city-level efforts to transform toward low-carbon, socially just, and ecologically viable economies and communities. This commentary shares an overview of the Wellbeing Economy, ways it has been piloted in cities to date, and key lessons for urban practitioners, policymakers, and residents. WEAll has developed a Wellbeing Economy Policy Design Guide that outlines practical, participatory processes for policymakers and their partners who want to bring Wellbeing Economy ideas to life. In 2021–22, it carried out city-level pilots in four locations: Pomona, California; Toronto, Canada; Porirua, Aotearoa New Zealand; and Perth, Scotland. Working with local governments and community partners, they took diverse approaches but nonetheless learned shared lessons. Across all four pilots, it was clear that such transformational policy work takes time, must be relationship-based, should embrace emergence, and is most effective when strength-based rather than problem-based.
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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 | 0.046 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".