Bibliographic record
Abstract
Partir avec un guide de conversation en poche permet d'enrichir son experience de voyage; connaitre les mots essentiels et mieux comprendre les gens du pays qu'on visite facilite les contacts, favorise les rencontres et procure un agrement additionnel indeniable. Le quebecois pour mieux voyager est un petit guide pratique et amusant qui permet d'apprivoiser et d'apprecier le francais tel qu'on le parle au Quebec, le joual, avec ses archaismes, ses regionalismes, ses expressions savoureuses, son accent et sa delicieuse spontaneite. Decouvrez l'histoire de la langue quebecoise et comprenez mieux ses mecanismes avec ce precieux outil a glisser dans votre valise qui comporte egalement des milliers d'expressions et de mots usuels pour voyager au Quebec, ainsi que la traduction de centaines de phrases a utiliser dans toutes sortes de circonstances, avec des indications phonetiques pour une prononciation appropriee. Des centaines de mots du francais quebecois, regroupes par centres d'interet y sont proposes (transports, sante, attraits touristiques, hebergement, restaurants, rapports humains, etc.). Differents themes propres au Quebec sont egalement abordes (mets typiques, emprunts a l'anglais, faux cousins, jurons, etc.). Avec sa presentation en couleurs renouvelee et rajeunie et son index detaille, le guide Le quebecois pour mieux voyager permet de trouver en un coup d'oeil ce que vous recherchez, en plus de prendre la forme d'un bel objet, agreable a manipuler et a conserver.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".