Making Pancakes: Intergenerational Cooking and Remediating the Archive
Bibliographic record
Abstract
Abstract In Making Pancakes, Alejandra Bronfman remediates the recipe Cakes para el Desayuno as a response to Aceves Sepúlveda’s great grandmother MamáPina’s cookbook. Along with her mother, Marisa Bronfman, her daughters Maia and Nina Dawson, and her partner Alec Dawson as the videographer, Bronfman sets up a cooking event in which three different generations interpret the recipe from their distinct subject positions. For this issue of Public, we propose to transcribe the intergenerational conversations that took place in Making Pancakes to unpack the ways knowledge is transferred from generation to generation and to reflect on the ways in which female domestic forms of writing and familial performative acts can be considered as central foci of the Archive. We will contextualize the transcription with still images from the video and a self-reflective response on cooking as remediation by Bronfman and Aceves.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".