You Taste Who You Are: The Impact of Wine Labeling on The Sensory Taste of Wine
Bibliographic record
Abstract
Within a culture, individuals may have difficulty distinguishing themselves or projecting who they want to be to both themselves and others. Consumers are able to express a self-definition through the systematic appropriation of goods that represent who an individual wants to be (McCracken, 1986). This study focuses specifically on the connection that consumption culture has to the formation of identity within the conceptual framework of consumption culture theory. Within this framework, consumers are viewed as identity seekers navigating opportunities in the marketplace that provide messages that embrace who they are (Askegaard & Linnet, 2011). Specifically, this research explores how relating to the visual sensory cues on a wine label can symbolically represent a desired identity, which may influence the sensory experience of wine.Five distinct research phases were used to address the central research question: do wine label visual sensory cues influence the perceived sensory taste of wine? Two online surveys, two in-person sensory evaluation tests, and in-depth interviews were conducted to explore the individual sensory impact of constructing an identity through consumption. As people seek to author their sense of self through the visual sensory cues that provide meaning to that person, there may be a cross-modal sensory association that influences the actual taste of the wine.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".