Bibliographic record
Abstract
Diet is one of the spheres that best shows us how a society operates and changes. That’s why a récipe book is always full of discourses. Nothing in cookbooks is superficial, gratuitous, or innocent. Studying them is like a compass, a time capsule that helps us identify ways the connections between people and the food they consume develop and understand cultural behavior. They are exercises in literary prose, and we can find in them not only guides about how to cook, but also a variety of socio-political and cultural discourses that show us an entire contextual analysis. For example, the first cookbooks in Mexico were written with nationalist aims at a time when people were seeking independence. Almost two centuries later, cookbooks are being written to document acts of extreme violence, such as forced disappearance and gender-based violence. The few materials of this kind that exist until now attempt to dismantle hegemonic discourses that were implanted years ago, breaking with the canon and the notion people have about cookbooks. Therefore, in this article, the aim is to analyze how the argument has changed in cookbooks, mainly those created in Mexico in the midnineteenth century and the break in the discourse found in those of the first quarter of the twenty-first century.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 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".