Bibliographic record
Abstract
The following chapters were written by a group of individuals engaged in an ongoing series of conversations about Canadian food culture and culinary history.One such conversation took place in Montreal, in November 2005, at the conference on "The Daily Meal in Canada" held at the McCord Museum, of which Victoria Dickenson, the museum's director, and I were co-organizers.But there have been many other conversations as well -over the telephone, by e-mail, in person -including a wonderful week in Toronto in November 2007, when a group of twelve people, led by Barbara Ketcham Wheaton, gathered for a workshop on cookbooks.Without these encounters and the insights emerging from them, this book could never have taken shape.To Victoria Dickenson, I owe my first debt of thanks.In addition to co-organizing the 2005 conference, she was also a key member of the organizational team for the "What Are We Eating?" conference on Canadian food policy and the shaping of Canadian food tastes, which was hosted by the McGill Institute for the Study of Canada in February 2006.Victoria's expertise, range of knowledge, astute observations, and infectious enthusiasm propelled this book project into reality.Our research team co-investigators helped this initiative in many ways as well.Marie Marquis, from Université de Montréal's Department of Nutrition, brought a new voice to the conversation, as did Jordan LeBel, from Concordia's Molson School of Business and Cornell University's School of Hotel Management.Another research team member, Rhona Richman Kenneally of Concordia's Department of Design and Computation Arts, provided constructive feedback and posed two particularly thought-provoking questions upon reading an early draft of the introduction.My thanks to all three.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.293 | 0.180 |
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".