El tratado médico-culinario como género de ficción en la narrativa hispanoamericana actual: Héctor Abad Faciolince y Mayra Santos-Febres
Bibliographic record
Abstract
Este ensayo ofrece, tomando como marco teórico la llamada gastrocrítica, un recorrido historicista por los tratados médico-culinarios clásicos y medievales para analizar la influencia de éstos en Tratado de culinaria para mujeres tristes de Héctor Abad Faciolince y en Tratado de medicina natural para hombres melancólicos de Mayra Santos-Febres. Se sitúa así a ambos escritores como evocadores o imitadores de la tradición secular del tratadismo médico y culinario haciendo énfasis en la necesidad de poner en relación periodos aparentemente inconexos para un análisis literario más provechoso. Palabras clave: gastrocrítica, tratadismo culinario, novela hispanoamericana Using the theoretical framework of gastrocriticism, this essay traces a historicist route through classic and medieval medical-culinary treatises to analyze their influence on Héctor Abad Faciolince’s Tratado de culinaria para mujeres tristes and Mayra Santos-Febres’ Tratado de medicina natural para hombres melancólicos. Both writers evoke or imitate the secular tradition of medical and culinary treatises; an analysis of these contemporary authors through the lens of works from an earlier era emphasizes the possibilities for literary criticism that identifies intersections of thought across various periods. Keywords: gastrocriticism, culinary treatises, Spanish American novel
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".