Projecting complex categories: biodynamic wine producers’ online identity
Bibliographic record
Abstract
Purpose This paper aims to investigate how producers of biodynamic and sustainable wine portray their brand identity online. Design/methodology/approach The study uses an inductive approach to qualitative content analysis of wine producers’ websites. The authors use a theoretical starting point based on the categorizations literature related to institutional scripts and identity projection. Findings Producers adopt identity templates similar to the provenance and glory templates established in extant research. They demonstrate templates of community, quality, spirituality and sustainability, but there is a break in the templates, and they adopt a pseudo-rationalist template, avoiding detailed descriptions of practices and underpinning philosophy, leaving any references to them opaque and ambiguous. This may be due to concerns over scientific skepticism or spiritual suspicion, or anticipation of a lack of consumer knowledge. Research limitations/implications The geographical location of the sample poses limitations to the results of the study. However, the study provides an examination of the nuances of self-categorization as it relates to identity projection, prompting further investigation into its positive and negative potential. Practical implications Research on the connection between quality perceptions and experience and credence attributes suggests producers should do more to emphasize the philosophy underpinning biodynamics. Originality/value The study contributes to research on marketing for inherently sustainable producers who may suffer potential negative reactions in general and biodynamic wine producers in particular. This study provides nuance to the understanding of negative reactions to novel and innovative wine production practices.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".