Boire et manger à Toulouse : des métiers de bouche à l'alimentation d'une ville médiévale
Bibliographic record
Abstract
From the 12th century onwards, Toulouse saw the development of a range of activities linked to the production and sale of foodstuffs, which played an important role in the urban landscape. Because these activities were essential to supplying the town, they were regulated very early on by the consulate, which tried to assert its power over the city in opposition to the representatives of the count and then the king. Guilds emerged, based on the practice of the same activity. Some enjoyed greater recognition from the consulate, which granted them privileges and assigned them representatives to whom it delegated some of its supervisory powers. But it’s not just the guilds that provided food for the town: many vendors came from outside the city, and the inhabitants also relied on their own production. The food eaten by the people of Toulouse at the end of the Middle Ages can be studied through its many regulations. Accounting sources can also be of great use in tracking food purchases by various communities, in particular the consulate. Then notarial registers, which provide information about the equipment used by professionals, about their living conditions and the relationships they have with people around them. Because of its political, economic, social, technical and symbolic dimensions, food can be seen as a "total social fact" that can be used to explore all the facets of society at the time.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".