Mobilising community participation and engagement: The perspective of regeneration professionals
Bibliographic record
Abstract
Literature suggests that the successful transformation of inner city areas is dependent upon regeneration professionals closely involving local residents, within a sprit of genuine partnership working. Yet urban regeneration programmes have been largely criticised for the way resident engagement and participation have been conducted, leading to debate on the requisite skillset of the regeneration professional. Undertaking semi-structured interviews with regeneration professionals in an area of the north-west of England with an established urban regeneration company, this paper examines the challenges that professionals encountered facilitating community involvement. The findings identified professional, institutional and organisational barriers that prevented regeneration professionals from mobilising the necessary tools, expertise and knowledge to practise effective engagement, including the absence formal training, limited opportunity for peer-to-peer reflection and the lack of role freedom. Addressing these barriers is fundamental to ensuring that regeneration professionals can share and negotiate meaningful forms of community participation and engagement.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".