An analysis of consumer perception, emotional responses, and beliefs about mead
Bibliographic record
Abstract
Abstract Mead is an ancient alcoholic beverage that lacks a large market share in Canada. This study aimed to identify consumer perception of mead, which sensory properties lead to liking and disliking of mead, and how participants would consume mead in their everyday lives. A sub-objective was to evaluate the use of a written scenario on consumers' liking and emotional response to mead. Alcoholic beverage consumers (n = 122) were recruited to evaluate six commercial mead samples. Initially, consumers completed a word association task about mead. Then, the participants were split into two groups; one group evaluated the samples after writing a consumption scenario, and the other without a scenario. The participants evaluated the samples using hedonic scales and check-all-that-apply questions (sensory properties and emotional responses). Findings showed that mead was associated with historical references and honey, as well as different sensory properties and other alcoholic beverages (beer, wine, and cider). Consumers preferred meads with higher alcoholic content, and meads that they perceived to be sweet and have floral and apple flavours. The use of the written scenario increased hedonic scores for flavour, appearance, and overall liking, as well as their selection of positive emotions when evaluating the mead samples. This study identified consumers prefer meads that are sweet and have a higher alcohol content.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".