Communicating pesticide contamination messages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it