Great wine from the great white north? Producer’s product positioning and marketing mix for Canadian icewine
Bibliographic record
Abstract
Icewine is a sizable niche in the Canadian wine industry that has attracted little attention from marketing and branding researchers. A first step in understanding the marketing mix and brand positioning strategies was to develop a modified Aesthetics and Ontology (AO) framework to classify consumers of luxury wines and spirits specifically focused on icewine. This paper examines where Canadian icewine producers place their brand and consumers within this AO typology. The authors applied a thematic analysis approach to categorize five semi-structured interviews with representatives of Canadian icewine producers. The modified AO framework was applied to the findings to assess the positioning of the respective icewine brands. The analysis uncovered decidedly homogenous approaches to the positioning and marketing of Canadian icewine. Most purchasers were regarded as novices, with the largest portion of purchases occurring at duty free retail locations; on-site winery experiences comprise a secondary channel. Applying the modified AO framework, the predominant customer group was identified as the ‘carouser’. Product variances, pricing strategies, and product packaging were comparatively minor. This homogenous approach to branding and marketing mix should be further explored to understand the potential for alternative and distinct positioning methodologies for Canadian icewine producers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".