English version of the food disgust scale: Optimization and other considerations
Bibliographic record
Abstract
Abstract The disgust elicited by food plays an important role in food choice and consumption. Recently, Hartmann and Siegrist (Food Quality and Preference, 2018, 63, 38–50) developed and validated in German the food disgust scale (FDS), a 32‐item instrument designed to measure visceral disgust elicited by food. In Study 1, we tested the English language translation of the FDS and its shortened version (FDS‐SHORT) in England (n = 85) and Canada (n = 70). The internal reliability (Cronbach's alpha and mean interitem correlation [MCI]) was acceptable for both the FDS (α = .90, MIC = .22) and the FDS‐SHORT (α = .73, MIC = .25). Exploratory factor analysis revealed that the English and German versions of the FDS had similar underlying structure and good discriminant validity. In Study 2, female participants (n = 159) who completed the FDS where the anchor term disgusted was used had higher FDS‐SHORT scores than either their male counterparts or females for whom the anchor term grossed out was used (F[2, 266] = 11.1, p < .001). As grossed out captures only visceral rather than moral disgust, we recommend its adoption in English versions of these scales. These studies confirm that, as modified, the English FDS and FDS‐SHORT are reliable and can be used with confidence in future research. Practical application This study has further assessed and optimized an English translation of the food disgust scale, which will allow for its use by food researchers and practitioners in English‐speaking countries. The finding that food disgust scores vary with sex and culture provides guidance to producers and marketers of novel food products and flavors.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".