Consumer preferences concerning potentially unsafe food
Bibliographic record
Abstract
BACKGROUND: Recent studies have shown that the reasons behind consumers’ preferences towards certain food products are extremely dynamic. Organic foods, raw milk products and bottled water are a few products discussed in this paper that have gone under debate regarding their safety versus their perceived health benefits. METHODS: Over 100 people participated in an exclusively online self-administered questionnaire. The questionnaire was publicized through both email and social media. Participants responded to questions regarding their food preferences of a variety of food types. RESULTS: It was found that there was a statistically significant association between education and preferences towards both milk products and organic/non-organic food products. No other demographic (setting, gender, age) were found to be associated with food preferences. It was also found that all food preferences were associated with the reasoning for that specific food preference, with the exception of cut/whole fruit. CONCLUSION: The association between food preferences and its reasoning concludes that consumers who prefer opposing products do so for extremely different reasons. Consumers that prefer the more risky food products mainly do so for taste and potential health benefits. Public health officials need to ensure that consumers that prefer riskier products thoroughly understand the risks, so that they themselves can then truly compare the benefits of taste or perceived “healthiness” with the consequences of potential contamination and illness.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".