Bibliographic record
Abstract
In African American history, food has functioned as expression of colonial power and control, and as a source of Black celebration and liberation. Cookbooks written by Black women from the mid-eighteenth century to late twentieth century reflect the long history of the development of African American cuisine. These texts are practical and instructional, while also offering insights into the transnational development of food as an expression of cultural history through African, Indigenous, and European influences. African Americans, and more specifically Black women, have contributed to the food history of the Southern United States by developing a distinct African American cuisine and creating the texts by which to publicly declare their knowledge and ensure its survival. By analyzing the cookbooks of Malinda Russell, Edna Lewis, Vertamae Smith-Grosvenor, and Carole and Norma Jean Darden, a timeline of cookbooks from the Civil War to the Black Power Movement can be established. Their commonalities, including the use of cookbooks as autobiographies, community memoirs, and genealogical records, are features that resonated with the Civil Rights Movement in the latter half of the twentieth century. Food is more than a means of survival. It is a constantly evolving expression of culture, people, and celebration.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".