Bibliographic record
Abstract
Between 1893 and 1901, the Parisian traiteur Potel et Chabot catered a series of gala meals celebrating the recent Franco-Russian alliance, which was heralded in France as ending its diplomatic isolation following the Franco-Prussian War. The firm was well adapted to the particularities of the unlikely alliance between Tsarist Russia and republican France. On the one hand, it represented a tradition of French luxury production, including haute cuisine, that the Third Republic was eager to promote. On the other, echoing the Republic’s championing of scientific and technological progress, it relied on innovative transportation and food conservation technologies, which it deployed spectacularly during a 1900 banquet for over twenty-two thousand French mayors, a modern “mega-event.” Culinary discourse therefore signaled, and palliated concerns about, the improbable nature of the alliance at the same time as it revealed important changes taking place in the catering profession.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Cultural history of Parisian catering and Franco-Russian gala banquets; the object is culinary and diplomatic history.
This historical study concerns French catering, diplomacy, and technology rather than research practice.
Cultural history of Franco-Russian diplomatic banquets and catering; not research practice.
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 | 0.009 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".