People respond to GM food with disgust more than fear: Comment on Royzman, Cusimano and Leeman (2017)
Bibliographic record
Abstract
Abstract Scott, Inbar and Rozin (2016) reported an association between greater disgust sensitivity (DS) and more negative attitudes towards genetically modified (GM) food. Royzman, Cusimano and Leeman (2017) replicated this association but argued that fear, not disgust, is the primary emotion associated with negative GM food attitudes. We believe that this conclusion is premature, because the “high-granularity” disgust scale variants on which it is based are theoretically and statistically questionable. We explain the issues with these disgust scale variants and report direct evidence showing that people respond to GM food with disgust more than fear.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.034 | 0.048 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".