The Wellbeing Economy Forum in Reykjavik: in search of alternatives to Davos and the far-right
Bibliographic record
Abstract
The 2024 Wellbeing Economy Forum highlighted diverse perspectives on the creation of a wellbeing economy (WE), which, in general terms, aims for sustainable wellbeing for people and planet and, for some supporters, represents a post-growth alternative to neoliberal capitalism. The event was hosted by the government of Iceland, one of five Wellbeing Economy Governments (WEGo) with shared ambitions of creating a WE, and also attracted representatives from major international organizations, WE activists, academics, and others. This Brief Report is based on participant-observation at the Forum, supplemented by a review of publicly available videos of selected presentations, and also informed by existing literature on the WE and WEGo. It provides an account of the state and range of WE thinking illustrated at the Forum, including the degree to which post-growth thinking was present, whether signs of a rightward drift among some WEGo nations under new conservative leadership were visible, and consideration of the evident strengths and limitations of the WE concept. The Forum took place days after a surge in support for far-right parties in European Parliament elections, which cast a shadow over the event and raised a pressing question: can a WE be a unifying concept to resist the rise of the far-right while addressing social and ecological crises? Although it is impossible to answer this question definitively, the Brief Report concludes by arguing that a WE vision that offers minimal change is not up to that task, but a more ambitious and transformative WE project might be.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".