Beyond “No food and drink in the gallery”: Writing a best practices document for food management in museums
Bibliographic record
Abstract
Abstract In 2015, the Society for the Preservation of Natural History Collections (SPNHC) Conservation Committee created a best practices document for food management in collection-holding institutions. This paper discusses the three-step process, devised by the committee, through which this was achieved. The first step was to research existing literature on the subject. Scant results showed that a best practices document on the subject would be of great benefit to the field. The second step was to survey collection professionals. This provided the committee a stronger understanding of current food management challenges and successes, as well as topics to address in the best practices document. The third step was to gain consensus from these professionals. A draft of the document was presented at three international conferences, and feedback was incorporated into the final recommendations. The best practices document is available on the SPNHC wiki and may be updated. It is possible to write a best practice on any subject by replicating this three-step process. The Conservation Committee believes this process can be applied to other areas that are in need of new or revised preservation methods.
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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 | 0.107 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".