OBSERVING CHANGES IN CANADIAN DEMAND FOR FOOD DIVERSITY OVER TIME
Bibliographic record
Abstract
Research on food diversity is interdisciplinary in nature, and is highly relevant for different research fields. Eating a variety of foods has been linked to the nutritional well-being of the household. From an economic perspective, food diversity can be used to derive important conclusions regarding the economic well-being of a population under study. This paper attempts to fill two main research gaps. The first objective of this paper is to analyze the demand for food diversity in Canada for the first time. This includes observing the extent of food diversity and the identification of respective socio-economic determinants. The second main objective is to compare changes in the cross-sectional demand for food diversity over time using three data sets of the Canadian Food Expenditure Survey (1984, 1996 and 2001). Food diversity is measured twice, with a measure used in nutritional studies and an economic diversity measure to draw conclusions for both research fields. Results show that in all years the demand for diversity (both indices) is positively influenced by income, age, and household size. We observe a significant quadratic influence of income in all models. Over all years, males and singles have a lower demand for food diversity than females and married Canadians. In addition, the region the household lives in is a strong predictor of food diversity. We observe changes in demand for food diversity in Canada. It is shown that the demand for food diversity decreased from 1984 to 1996 and 2001.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".