Diverse Collections, Different Contexts: Risk Management for the Oswaldo Cruz Foundation’s Cultural Heritage
Bibliographic record
Abstract
Abstract The article aims to reflect on the importance of implementing risk management for the conservation of diverse collections in different contexts. The discussion is based on the experience of teams at the Oswaldo Cruz Foundation in Rio de Janeiro, Brazil, a century-old institution devoted to research and development in public health and that has various types of historical and scientific collections in its custody. From 2014 to 2018, Casa de Oswaldo Cruz, the unit in charge of the foundation’s heritage, conducted a pilot experience in the implementation of risk management for the Fiocruz collections, adopting the ABC Method developed by the International Centre for the Study of the Preservation and Restoration of Cultural Property (ICCROM) and the Canadian Conservation Institute (CCI). The result of this pilot experience was essential for expanding knowledge of the different risks that can impact the foundation’s cultural heritage and for proposing mitigation measures. The COVID-19 pandemic brought new challenges for the institution and required rethinking strategies to guarantee the safety and security of collections, employees, and users, considering contextual changes that included alterations in the uses of the sites where the collections are located and the ways of accessing them.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".