Do consumers notice the source of messages about foodborne illness outbreaks?
Bibliographic record
Abstract
Authorities identified a widespread outbreak of E.coli 0157H7 associated with romaine lettuce in Canada and the US during the winter and spring of 2018. While the public health agencies of each country acknowledged the outbreak, there was no official recall of romaine lettuce. However, the Consumers Union, an advocacy group that publishes Consumer Reports, called for American consumers to avoid romaine lettuce, and this was widely reported in the news. Given how unusual it is for an advocacy group to provide this type of guidance while the government did not, we decided to conduct an experiment to see if American consumers would notice. The current research used an online survey with a nationally representative sample of over 1400 American adults. Data were collected in May and June, 2018, shortly after the E.coli outbreak had ended. Participants were presented with a hypothetical scenario about an E.coli outbreak that mimics the romaine lettuce outbreak, except they were told that the affected food was cucumbers. The group announcing the guidance was experimentally varied (US Food and Drug Administration, Canadian Food Inspection Agency, or Consumers Union). The study tested whether American consumers notice the source of food safety advice. The study also tested whether, for those who do notice the source, behavior differs by source, and how trust in the source affects behavior. We also looked at demographic and experiential factors that might affect who is aware of the message source, and whether those same variables are related to likely behavior.
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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".