Happiness on your doorstep: disputing the boundaries of wellbeing and localism
Bibliographic record
Abstract
This paper is a critical review and analysis of the recent emergence of wellbeing discourses in UK national politics and their relationship with localism agendas. In 2011 the UK Coalition Government initiated a national programme to measure wellbeing. Despite a stated desire to consult the public as widely as possible on what matters for wellbeing, policy discourse is currently dominated by particular framings of wellbeing, predominantly within the arenas of subjective wellbeing research, positive psychology and individual behaviour change, where community participation and volunteerism narratives feature heavily. Ideas of wellbeing are enmeshed within narratives of reducing bureaucracy and creating the Big Society. This argument is backed up by a discourse analysis of government documentation on wellbeing and localism, which illustrates how discursive boundaries are being created around the concept of wellbeing which in turn demarcates clear boundaries of responsibility. The explicit desire on the part of the UK Coalition Government to devolve more responsibilities to the ‘local community’ is justified by appeals to particular ideas of wellbeing which are evidenced by particular sorts of research, limiting room for other, more progressive, accounts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.067 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".