Using Preferred Attribute Elicitation to Determine How Males and Females Evaluate Beer
Bibliographic record
Abstract
The variety of beers available for consumption has increased due to the recent emergence of many craft brewing operations and it has been suggested that this is affecting how consumers evaluate beer. Currently, beer consumers are mostly male and only 20% of women are primarily beer drinkers. The main objective of this project is to compare and contrast descriptions of beer products created by males and females. The preferred attribute elicitation (PAE) method was used to create a description of 4 beers common to residents of Nova Scotia, Canada. Four PAE sessions were held: 2 sessions consisted of females (n = 16 and 15) and 2 sessions of males (n = 11 and 17). Four beer samples were chosen from locally available commercial beers, 2 of these samples were considered to be craft-brewed beer and the other samples were nationally available brands (macrobrewed). Both the males and females generated descriptions that included 5 identical terms; however, they differed in the importance they assigned to each attribute. Notably, bitterness was perceived to be of more importance to female panelists. Throughout all PAE sessions, the craft-brewed beers were associated with considerably more sensory attributes than the macrobrewed beers. It can be concluded that both the female and male groups found discernible differences between the craft and macrobrewed beers; however, they place importance on different sensory attributes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".