Preventive Conservation on Demand: Developing Tools and Learning Resources for the Next Generation of Collections Professionals
Bibliographic record
Abstract
The modern learning environment is evolving at a rapid pace. Technology can help developers of preventive conservation tools and learning resources for collections professionals to increase their impact and reach. However, it is crucial to keep the needs of users, and gaps in skills and knowledge at the forefront. This article examines preventive conservation tools and resources developed by the Canadian Conservation Institute (CCI) and ICCROM (International Centre for the Study of the Preservation and Restoration of Cultural Property) over the past 30 years. In light of the results from a recent survey and research in the learning and development field, a set of orientations for future tool development are highlighted; these tools must be: need driven, user centered, emulating everyday experiences, social and informal, concise, mobile friendly, curated and open access.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".