Delivering the National Trust's preservation purpose: mission, strategy and structure
Bibliographic record
Abstract
Whilst reporting on numerical-based activities such as income versus expenditure is relatively straightforward for organisations seeking to understand their progress in meeting their objectives, it is more difficult to present nonnumerical-based activities such as conservation and preservation in similar terms. However, without numerical key performance indicators (KPIs) conservation activities risk becoming invisible in organisational reporting, compared to activities that are easier to measure, such as, in heritage organisations that open their assets to the public, visitor numbers and income. The National Trust for England, Wales and Northern Ireland has devised a numerical measure to describe relative performance in reaching defined conservation standards, benchmarked against external standards such as the Canadian Conservation Institute's 'Agents of Deterioration' framework and the UK's Museum Accreditation Scheme. This paper describes the development and practice of this measure, the Conservation Performance Indicator (CPI), with particular reference to cultural heritage features, such as historic interiors, collections, buildings and archaeology, and in the context of the evolution of a devolved structure.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".