Bibliographic record
Abstract
Eating is one of the most innate human instincts. People need food to survive, and food and the culture around eating have been transformed into indicators of personal, community, and national identity. As an often-overlooked branch of social history, food history provides a critical look into the social nature of our nation’s past, as well as the effect of food on the culture and politics of Canada. It is for these reasons our course has decided to mount an exhibit in Special Collections on the history of food in Canada, showcasing the Library’s historical cookbooks. We will be structuring our exhibit through the lens of the following critical themes: Food Nationalism, Women and Community, Multi-culinary-ism, Business of Food, and Local Food Sources in Kingston, using cookbooks dating as far back as the 19th century. These will be brought together to share a glimpse into the study of food history and its importance within Canadian Social History. Sifting through the impressive assortment of cookbooks that W.D Jordan Rare Books and Special Collections has to offer will lead to the selection and presentation of 25 cookbooks that are an essential representation of the food history of the nation. Representing our classmates, we hope to share an overview of our research with Inquiry@Queen’s and promote our exhibit, which opens two weeks after the conference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.264 | 0.082 |
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".