Townscape Heritage Initiatives Evaluation: Methodology for Assessing the Effectiveness of Heritage Lottery Fund Projects in the United Kingdom
Bibliographic record
Abstract
As a result of various social and economic factors, many historic townscapes in the United Kingdom and elsewhere have declined over the last half century. There have been many attempts throughout the world to revitalise such urban heritage areas, but the actual effectiveness of few of these schemes has been systematically evaluated. Good public policy choices would greatly benefit from such evaluation. The UK's Heritage Lottery Fund (HLF) decided in 1999 that their Townscape Heritage Initiatives would be an exception. A research team from Oxford Brookes University was engaged to undertake a ten-year study of the £52 million being spent in about sixty British towns and cities. A sample of about one third of projects receiving HLF support are being scrutinised. The evaluation methodology is outlined, along with explanations of some challenges faced in such a large programme. The four mechanisms for gathering research data are explained, the origin and rationale for the sixteen indicators being employed are described, and the approach to overall evaluation outlined. Finally there is an overview of the baseline stage of the work, highlighting key issues from a research perspective, and briefly reflecting on findings to date.
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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 | 0.042 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".