Des mets et des mots : nommer les mets dans la littérature du XVIe siècle espagnol
Bibliographic record
Abstract
The Renaissance believed in the creative power of names. Particularly in 16th-century Spain, there was an effort to put words on a whole series of new realities, particularly those of the New World, where the discovery of new vegetal, animal and mineral species multiplied the need for new terms, or for broadening the scope of those that already existed. Literary texts largely integrated this requirement. Literature was thus going to name the world, especially in a space to which it granted a renewed place: the table, all the more so since the meal makes it possible to immobilise people momentarily, which makes it possible for language to unfolding with greater ease, to really invest the space. Sitting at the dinner table sets the conditions necessary for a discussion to take place. The authors at the time therefore often resorted to it. At the table, the man discusses the most diverse subjects from all over the world. But, inevitably, what he is looking at in front of him, the food, will prevail in his reflection. Hence a marked interest in food vocabulary in the texts, particularly through a real fascination for certain dishes. But naming the dishes also makes it possible to touch on issues that go far beyond food itself: what then do words that talk about dishes really talk about? This is what we will try to identify here.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".