Risk Perceptions and Food Safety Enhancing Technologies –\nDoes Information Matter?
Bibliographic record
Abstract
The recent cases of human E. coli infections linked to Chipotle Mexican Grill in mid- October to November of 2015 brought the issue of food safety into the limelight. The outbreaks which were first detected in the Seattle Washington and Portland Oregon areas, were also reported in 7 other states, altogether leaving about 50 persons infected. Following a report about the outbreak by the Centers for Disease Control, Chipotle's sales for the last quarter of 2015 plunged by nearly 15% (Bloomberg News, Jan 6, 2016), adding to other costs incurred due to the outbreak such as medical expenses of the individuals infected and productivity losses. Without doubt, news from the media that raises awareness about compromises in food products reverberates among consumers. Consumer attitudes and responses towards food safety issues are influenced by their implicit biases, unique predispositions, and their perceptions of food safety risks. News about food safety compromises in the media, and other information sources may amplify such consumer predispositions.
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 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".