Irish consumer perceptions of meat hazards and use of extrinsic information cues
Bibliographic record
Abstract
The purpose of this research was to examine the factors that underpin risk perceptions for meat hazards and assess the degree to which these perceptions reflect overall concern. It also sought to identify any differences that exist in information use. A total of 238 meat customers were surveyed in Cork, Ireland. An analysis of consumer perceptions revealed a two-factor structure, ‘dread’ and ‘unknown’, similar to Slovic (). A perceptual map of the various hazards associated with meat clearly highlighted different groups of hazards and the differing perceptions associated with them. There were groupings between BSE, E-coli and Salmonella, antibiotics, growth hormones and genetic modifications, and saturated fats and cholesterol. Cluster analysis highlighted the differences in attitudes towards meat hazards across the sample population. However, an analysis of the demographic and behavioural variables found no distinguishable features across the identified segments, except in their use of written information. The butcher, quality assurance (QA) marks, country of origin and labelling were identified as the most helpful risk relievers. However, no significant differences were identified between perceived helpfulness and level of perceived ‘dread’ and ‘unknown‘. Use of written information by consumers (labels, quality marks and information brochures) was significantly related to their overall concern about hazards.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".