Planning for when Push Comes to Shove: Mitigating Risk; Recovering from Disaster.
Bibliographic record
Abstract
Established in 1854, Museums Victoria manages and develops collections in the disciplines of natural sciences, indigenous cultures, and history and technology. The natural sciences component consists of about 17 million specimens across the disciplines of zoology, palaeontology, and geosciences. Over the past 7 years, Museums Victoria undertook a range of projects to mitigate risk and respond to disasters. These projects were driven by the Strategic Collection Management group but involved a multidisciplinary team including Collection Managers, Researchers, Conservators and Facilities Managers. The results informed strategic and workforce planning and resulted in successful bids for significant project-based funding to address areas of greatest risk.\n These projects include:\n \n A Collection Risk Assessment and Management project (CRAM), based on the methodology developed by Robert Waller (formerly of the Canadian Museum of Nature), which examined vulnerability of collections to 10 agents of risk. This project identified over 100 actions to reduce risk, including mitigation measures on the micro level (e.g. creation of microclimates for specimens), the macro level (e.g. targeting registration and location control resources) and the grand scale (e.g. development of new storage facilities).\n \n Through prioritising outcomes by risk impact, additional funding was secured to relocate collections from inappropriate building facilities; genetic collections were rehoused from freezers into a liquid nitrogen cryobank facility, the first of its kind in an Australian museum. Risks associated with security issues and with the disassociation of data from specimens are being addressed by audit processes and providing additional funding for registration projects. \n \n A compliance project to ensure 'approved borrower' status and protection under the recently enacted Australian Federal Government anti-seizure legislation: the Protection of Cultural Objects on Loan (2013) (PICOL). Museums Victoria has reviewed acquisition and loans policies and procedures to ensure best practice in loans policy, provenance checking, and due diligence research.\n The development of venue-specific Disaster Plans based on an all-hazards approach.\n \n At the institutional level, the Museum has a Crisis Management Plan and a Business Continuity Plan.\n Experience shows Push will inevitably come to Shove: Risk management requires a strategic approach with planning, training and regular review essential. Plan to prevent, plan to prepare, plan to respond, and plan to recover
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".