Buzzword or breakthrough beyond growth? The mainstreaming of the Wellbeing Economy
Bibliographic record
Abstract
A wellbeing economy (WE) has been promoted by many advocates of a post-growth economy. Drawing on the growing WE literature, including detailed case studies of Wellbeing Economy Governments (WEGo), the article asks: does growing support for a WE represent a breakthrough for post-growth economic ideas? Or has mainstreaming the WE concept emptied it of radical post-growth content? The WE experience is interpreted in light of an earlier debate in the international development community over the mainstreaming of radical concepts that were purged of transformative meanings – becoming buzzwords that did not fundamentally alter existing practices. Wellbeing and the WE similarly risk becoming buzzwords: feel-good ideas that are hard to oppose, but which users can fill with their own meanings and political agendas. The WE's post-growth roots are contrasted with the pro-growth meanings given to it by organizations including the OECD and WEGo nations. The WE has nevertheless shown some promise in enabling limited steps in a post-growth direction in WEGo nations (within a broader context of continued pursuit of growth). The article presents – and invites debate on – some possible responses to resist dilution of the WE concept and advance a transformative post-growth vision. • The Wellbeing Economy (WE) has found considerable mainstream support. • Despite having post-growth roots, the WE has taken on pro-growth meanings. • The WE risks becoming an empty buzzword as it is mainstreamed. • Wellbeing Economy Governments have not moved beyond economic growth. • Possible strategies are proposed to strengthen the WE's post-growth character.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.022 | 0.035 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".