A heuristic model to identify and measure the perception of success in a nascent wine industry
Bibliographic record
Abstract
The main purpose of this paper is to identify the main factors which contribute to the perceptions of success in the wine industry of Nova Scotia, Canada. Commercial winemaking in Nova Scotia is a nascent industry. An investigation into this regional industry can certainly benefit the local winemakers and help indentify commonalities for further research in other similar regions. The data used in the study is based on 17 different case studies related to this regional industry. These case studies are in the form of interviews with winemakers, winery owners and industry stakeholders. Local demand, expansion opportunities and growing conditions are amongst the main identified factors. \n \nUsing the identified factors, a heuristic model for determining an index for the success perception by the industry, was built. In addition to working out an index, the heuristic model can also help winery managers to perform what-if analyses by altering the weightings of the factors or compare their situation with other wineries. As part of the ongoing research, it is envisaged that further work to enhance this model will be carried out as part of ongoing research in this area.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".