Wine Consumption Determinants in Albania Using Categorical Regression Model
Bibliographic record
Abstract
The increasing trend of wine consumption in Albania has led the development of the respective subsectors, viticulture and the wine industry. In the order for the domestic wine production industry to be competitive, a detailed understanding of the consumer’s buying behavior is a prerequisite. To this end, this study offers an actual perspective of the consumption behavior of wine customers in Tirana region. One of the goals of this paper is to identify and quantify determinants of wine consumption by using a regression model called “Categorical Regression Estimation” for non-numeric response variables. A questionnaire has been designed for this purpose, which is based on the literature but also on the recognition of the customer profile in the country, considering several socio-economic factors. Through 230 face-to-face interviews, the aim is to evaluate the impact on wine consumption of income, age, education, religion, nutrition culture, wine prices, wine origin as well as other socio-demographic factors related to the profile of the consumer. The analysis and interpretation of the results reveal interesting factors that determine the wine consumption. Age, education, income level and price of the wine are the main factors affecting the consumer decision to buy wine. Older people (over 40 years old) represent 1.4 times higher willingness to buy wine relatively to the younger people. Meanwhile, among people with higher income level chances that they will buy wine are 2.15 higher relatively to the people with lower monthly income level. From the results appears that education have positive impact on wine consumption while gender does not represent a significant difference.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".