Teaching Risk Management of Collections Internationally
Bibliographic record
Abstract
Risk assessment and the purpose it serves, risk management, are widely adopted by business, institutions, and governments, seeking to minimize future losses of all kinds. If the preservation goal of museums is stated as the delivery of the collection to some future point in time with as little loss in value as possible, then risk assessment and risk management provide the only rational means to reach this goal. Difficulties arise due to uncertainty and complexity. A three-week course on this method has been designed, and recently offered, by ICCROM (the International Center for the Study of the Preservation and Restoration of Cultural Property) and the Canadian Conservation Institute (CCI), with the collaboration of leading experts from the Canadian Museum of Nature (CMN) and the Netherlands Institute for Cultural Heritage (ICN). Demand for the knowledge was strong, as shown by the number and diversity of applicants worldwide. Great care and effort was taken with the design of the learning process and the supporting resources, in order to overcome the known, and profound, challenges of the subject. The result has been successful.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 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".