Teaching the counter story. An analysis of narration in African American cookbooks using Critical Race Theory
Bibliographic record
Abstract
As nutrition educators we must promote sensitivity to the historical roots of eating and food patterns. This analysis of narratives from a sampling of cookbooks written by African Americans, represents an attempt to give voice to an unconventional source of documentation regarding the historical experiences of a people oppressed by enslavement and institutionalized racism as told through recipe sharing. The themes that emerged from an examination of the missions and motivations of the authors included; history, work, cultural tradition, and empowerment in the struggle to survive. Critical Race Theory provided a lens to examine the counter story told by these authors. The counter story documented the unrecognized contributions of African Americans to the culture of all food practices in America, through their roles as cooks in domestic and industrial settings, as well as their own homes. We need to develop an appreciation of the celebration of life that is expressed through food in the African American community. And we must advocate for the right to good food, healthcare and education for all of the communities and people we serve.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".