Reply to three commentaries on the definition of sensory and consumer science by Jaeger et al. (2024)
Bibliographic record
Abstract
We respond to the three commentaries (Varela, 2025; Lee, 2025; Beckley et al., 2025) submitted in relation to our paper defining the field of Sensory and Consumer Science (Jaeger et al., 2024). Rather than attempting to address every aspect of these detailed and thoughtful commentaries, we concentrate on four discussion points: 1) Do we require a definition of our field?; 2) What should our field be called?; 3) Is non-food part of our field?; and 4) What is the nature and scope of our field? The prevailing view supports the importance of defining the field. To remain relevant, we must revise and update the definition regularly. Over time, the field has broadened in content, and this trend may persist. There is room for ongoing discussion on how to name the field and the central role of sensory (perception) science. Two of the commentaries were food-centric. While we agree that the food domain will continue to dominate the field due to its historical ties with food science, we also believe that non-food is part of our field and hope for growth in this domain. The field is broad and multidisciplinary and is evolving towards being interdisciplinary. We all agree this is a positive development. • Considers definitions for the complex and broad field of Sensory and Consumer Science. • Discusses authors' definition and that of three commentators. • The definition of our field is changing over time and especially changes in content. • The field is becoming more diverse and inclusive of both sensory and consumer research.
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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 | 0.050 | 0.283 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.068 | 0.081 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".