Begin with Benefits: Reducing Bias in Conservation Decision-Making
Bibliographic record
Abstract
The National Trust for Scotland (NTS) has undertaken a radical revisioning process via a collections and interiors review to protect significance whilst broadening support for conservation. This project reframes the consequences arising from a selection of current and possible uses of collections within an historic building. It creates a lifetime risk approach against which short-term activities can be benchmarked to inform decision-making at a local level. Instead of framing consequences from operation or more intense visitor patterns in terms of tangible change the project jointly conceives benefits across the mission of NTS allowing a direct comparison between benefits and consequences. A representative selection of current and possible use scenarios is being generated by the staff of Newhailes House, Edinburgh. A framework is presented in which the anticipated benefits from proposed activities will be identified and roughly quantified. Results of these assessments will be manipulated to create effective communication visuals. Psychology suggests that this will enable a more informed and balanced stakeholder engagement. This project fundamentally shifts the conservation discussion from permissive versus conservative conservation approaches, replacing the statements that ‘I am a no touch’ or ‘I am a please touch’ conservator with an evidence-informed and bias-reduced decision-making strategy.
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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 | 0.458 | 0.671 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".