Demonstrating Feminist Metic Intelligence Through the Embodied Rhetorical Practices of Julia Child
Bibliographic record
Abstract
The concept of metis reinserts the body and its intelligences into the ways in which rhetoric is understood, harnessed, and performed. Originating from the wisdom of Greek goddess Metis, the concept of metis is commonly understood as cunning intelligence that is deployed in order to escape an adversary, trick an opponent, or dupe its victim. When metic intelligence is read through the helping acts of Metis and her daughter, goddess Athena, however, an expanded version of its ways of operating begins to emerge. Through her efforts to debunk French cuisine using rule-based approaches meant to empower home chefs in their own kitchens, Julia Child, cookbook author, television educator, and chef, embodies metis with practices that represent feminist metic intelligence. In archival collections that reveal prolific correspondence, manuscript drafts, and television production material, Child deploys rhetorical strategies meant to teach and stimulate physical movements of cooking, and she does so by positioning the rules of cuisine as the gateway to culinary agency. For Child, structured rules allowed a home chef to respond to and recover from mistakes, and the rules also, consequently, fostered creativity and culinary freedom in the kitchen. Child’s own wielding of cunning and embodied intelligence helps us understand how metis is cultivated by a rhetorical body, and, in turn, Child’s metis may allow us to better understand how embodied rhetorics are invented and deployed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".