Using Preferred Attribute Elicitation to Determine How Males and Females Evaluate Beer
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it