How good (or how better…) do you think you are?:The strategic positioning of competing countries in key export markets
Bibliographic record
Abstract
The last twenty years have seen a fast internationalisation of wine markets across the globe and the rise of the new wine world, competing with traditional wine producing countries (OIV, 2010). In addition, the majority of wineries regularly export their products to generally more than one country nowadays (Crozet et al., 2009). It is, therefore, important to comparatively understand how consumers perceive wines coming from different Producing Countries (PC) in key Consuming Countries (CC), not only in relation to traditional intrinsic and extrinsic product attributes (Mueller et al., 2010a), their taste (Sirieix and Remaud, 2010), their value-for money (Orth, 2006), or matching with food (Casini et al., 2009), but they are also required to be safe, reliable, and environmentally friendly (Euromonitor International, 2010). This research asked more than 2,500 consumers recruited by an international consumer panel company in UK, Ireland, US, Canada, and Sweden to associate characteristics relative to the product dimensions mentioned above to five key PCs: Australia, Chile, France, South Africa and the US. Deviations from the expected values greater than 5% defined significant differences in perception by consumers in a specific CC to the different PCs. Results revealed that new wine producing countries were not seen as homogeneous but were perceived distinctively differently from each other in most CCs. During their market presence of more than ten to fifteen years in most export markets, new world wine producing countries have build up unique country images. At the same time our research confirmed a still existing strong divide in the profile between new world and the most prominent old world country, France, which had the most distinctive profile in all CCs. However, Australia has a relative competitive advantage in relation to Chile, South Africa or the US in most importing countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".