Urban regeneration and the mental health and well-being challenge: In support of evidence-based policy
Bibliographic record
Abstract
The rising attention given to mental health and well-being in urban policy, urban regeneration projects and place-making practices has led to an increase in the production of a supporting research evidence base. This paper presents a reflective review of a subset of this research, that which focuses upon urban mental health and well-being as they unfold in the context of relatively disadvantaged urban communities in the UK. Particular attention is given to research which interrogates the role played by the meaningful involvement of communities in decision making in cultivating good mental health. The paper concludes by identifying where evidence gaps still exist and where the evidence base might be improved.
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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 | 0.089 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 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".