Cooking Up Change: Family Cookbooks as Markers of Shifting Kitchen Politics
Bibliographic record
Abstract
Despite the recent academic attention community cookbooks have finally been granted, little has been said about compiled family cookbooks. Even works such as Janet Theophano's Eat My Words, Andrea Eidinger’s "Gefilte Fish and Roast Duck with Orange Slices": A Treasure for My Daughter and the Creation of a Jewish Cultural Orthodoxy in Postwar Montreal” and Marie Drews’ examination of In Memory's Kitchen, are about works by, and for, an entire community rather than for family. Furthermore, though gender critics have long documented the imbalance of food-related work in the home by showing that women have always been the primary food makers, one cannot deny that the makeup of modern families has changed and that men and children are becoming more active in the kitchen. Drawing on past and current literature to analyse a family cookbook I made and gave to my cousin for her wedding, this essay draws academic attention to family cookbooks and family food practices. While the cookbook I analyse is predominantly feminine, the many male and child voices included in this collection, voices that are usually excluded from such works, prove that, when given a chance, these often silenced groups can, and do, impact a family's food habits.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".