Socioeconomic Determinants of Health‐ and Food Safety‐Related Risk Perceptions
Bibliographic record
Abstract
Individual and societal perceptions of food-related health risks are multidimensional and complex. Social, political, psychological, and economic factors interact with technological factors and affect perceptions in complex ways. Previous research found that the significant determinants of risk perceptions include socioeconomic and behavioral variables. Most of these past results are based on two-way comparisons and factor analysis. The objective of this study was to analyze the significance of socioeconomic determinants of risk perceptions concerning health and food safety. A multivariate approach was used and the results were compared with earlier bivariate results to determine which socioeconomic predictors were robust across methods. There were two major findings in this study. The first was that the results in the multivariate models were generally consistent with earlier bivariate analysis. That is, variables such as household income, number of children, gender, age, and voting preferences were strong predictors of an individual's risk perceptions. The second result was that the gender of the respondent was the only variable found to be robust across all three classes of health and food safety issues across two time periods.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".