Bibliographic record
Abstract
Barbara was born in Philadelphia and after a brief study of Freshwater Biology, she studied Art History in Harvard. She married Bob Wheaton, a historian, and they moved to the Netherlands c. 1958 where they lived for over two years and where she discovered a book called La Cuisine de Madame E. Saint-Ange, and started cooking French food. On her return to Cambridge, she got a library ticket for Harvard and started reading the Menagier of Paris treatise on domestic management, which includes a big section on food history, or rather recipes. From here Barbara moved on to Medieval English Cookery books and then German and Italian and had the idea she would write ‘the history of European cookery from the middle ages to the present’. The book she finally published in 1983 was ‘Savoring the Past: The French Kitchen and Table from 1300 to 1789’. She was one of the founding members of the Culinary Historians of Boston. She first attended the Oxford Symposium on Food and Cookery around 1985 and has been coming nearly every year since. Barbara has always been fascinated with computers and from early days she had been building a database of cookbooks, first on punch cards and now on an elaborate system that she calls ‘the Sifter Project’. This project is based at Harvard University where Barbara is honorary curator of the Arthur Elizabeth Schlesinger Library on the History of Women in America at the Radcliffe Institute for Advanced Study. Barbara has been teaching a course called ‘Reading Historic Cookbooks: A Structured Approach’ for many years in Harvard, New York, Los Angeles, Toronto and Dublin.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".