Bibliographic record
Abstract
In Canada, the donut is often thought of as the unofficial national food. Donuts are sold at every intersection and rest stop, celebrated in song and story as symbols of Canadian identity, and one chain in particular, Tim Horton's, has become a veritable icon with over 2500 shops across the country. But there is more to the donut than these and other expressions of 'snackfood patriotism' would suggest. In this study, Steve Penfold puts the humble donut in its historical context, examining how one deep-fried confectionary became, not only a mass commodity, but an edible symbol of Canadianness. Penfold examines the history of the donut in light of broader social, economic, and cultural issues, and uses the donut as a window onto key developments in twentieth-century Canada such as the growth of a 'consumer society,' the relationship between big business and community, and the ironic qualities of Canadian national identity. He goes on to explore the social and political conditions that facilitated the rapid rise and steady growth of donut shops across the country. Based on a wide range of sources, from commercial and government reports to personal interviews, The Donut is a comprehensive and fascinating look at one of Canada's most popular products. It offers original insights on consumer culture, mass consumption, and the dynamics of Canadian history.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.033 | 0.016 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".