Home Cooking, Food Consumption, and Food Production among Retired Canadian Households
Bibliographic record
Abstract
Utilizing the 1996 Canadian Food Expenditure Survey matched with the Canadian Nutrient File, we attempt to differentiate between food consumption and the observed food expenditure among retired Canadians. We look at the effect of retirement on food expenditure, production, and consumption to test the universality of results obtained by Aguiar and Hurst (2005) from US data. In contrast to US results, conditional on a similar vector of covariates, we find no evidence of a fall in food expenditure following retirement. Our results suggest that the quality of food consumed by Canadian households improves somewhat with retirement. Similar to US results, we observe that household calorie intake and major nutrient intake seem not to be adversely affected by changes in retirement status. We find evidence that retired households substitute food purchased for consumption away from home for food purchased for at-home consumption. Further, using the 1998 Time Use Survey, we find that individuals who are retired devote more time for food preparation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".