Bibliographic record
Abstract
Abstract Over the last two decades, an increased understanding of the extent of pesticide contamination of organic collections in museums, particularly natural science and ethnographic collections, has developed. This paper explores the intellectual and emotional responses to messages about pesticide risks in museums and reports on the impact of wording on risk warnings. Six risk phrases using different terminology but intended to represent the same danger of pesticide contamination were evaluated by 103 museum staff. We found that how a message was delivered, the degree of science education of users, and phrases associated with hazards affected how a message was perceived. The delivery of risk warnings and the effective communication of collections-based hazards in museums are essential to responsible collections use, particularly those of scientific (Natural History) and cultural (Ethnographic) importance, where collections are most likely to be contaminated with hazardous substances. The results presented are a first step to understanding how the communication of pesticide risks in museums is understood by users of the collections. By understanding how a message is perceived, we provide advice to museum staff about language use for risk communication projects and management of behaviors.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".