Impact of Facility Renewal Deferment on Risk to Royal British Columbia Museum, Canada, Collections
Bibliographic record
Abstract
The Royal British Columbia Museum (RBCM), Canada, houses a collection of almost 7 million artifacts, archival records, and natural history specimens. Three comprehensive collection risk assessments over the past decade have resulted in improvements to the physical environments of the collections as well as new policies and procedures to reduce risk. However, there remain ongoing risks that can only be mitigated through major facility renewal. The last collection risk assessment, completed in 2016, was revisited to review the data and build a defensible case for funding to replace the RBCM's on-site collection storage facilities. Changes to overall collections risk is a complex function of collection development and use trends, evolving risk factors both internal and external to the museum, a growing understanding of the relationship between risks and preservation, in addition to reduction due to risk mitigation projects and building systems aging and wearing out. A defensible method for illustrating the facilities-related risks over time involves estimating the expected loss of individual collection items or loss in value of a group of items that may occur if a major facility upgrade or redevelopment is not realized in the near future. Risk assessment data for representative collection units were reviewed to differentiate risk due to permanent facility characteristics versus more active controls, operations budget controlled risk versus capital budget controlled risk, and collection management-controlled risk versus facility management-controlled risk. This enabled the risk model to isolate risks that could only be mitigated through major facility upgrades. Change in collection value was expressed as Object Equivalents Lost (OEL) and its compliment Object Equivalents Remaining (OER). Projections into the future indicating the effect of varying facility renewal dates could then be clearly shown. Losses, when presented as numbers of items expected to be lost from the collection, become emotionally salient to persons in senior management and governance roles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".