Édouard Montpetit en Californie (1918)
Bibliographic record
Abstract
Les récits de voyage et de migration comme modes de connaissance ethnographique : Canada, États-Unis, Europe (XIXe-XXe siècles)\nTravaux menés par une équipe franco-canadienne animée par Gérard Fabre, chercheur du Centre national de la\nrecherche scientifique (CNRS) à l’Institut Marcel Mauss, École des hautes études en sciences sociales, à Paris et\nYves Frenette, titulaire de la Chaire de recherche du Canada de niveau 1 sur les migrations, les transferts et les\ncommunautés francophones, Université de Saint-Boniface, au Manitoba.\nLes récits de voyage et de migration que nous sélectionnerons seront publiés au fur et à mesure de leur intégration au chantier La francophonie nord-américaine. Les premiers textes ont été intégrés à partir de 2018. D’autres, en préparation, suivront et élargiront le champ initial à d’autres périodes, milieux et types de scripteurs. Nous tenons à remercier le CIEQ pour son soutien irremplaçable et pour les compléments iconographiques et cartographiques\ndes récits présentés.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".