La neonymie a l’epreuve de l’ADT. Le cas du terme “francisation” au Quebec
Bibliographic record
Abstract
Abstract: Through a case study — the analysis of the discursive behaviors of the term “francisation” — the article proposes to show that any neological phenomenon in terminology does not arise only from a demiurgic act having its source in the practices of standardization. On the contrary, an important role is played by the spontaneous behaviors taking place in the speech. In order to understand the cultural and intercultural data that underlies the discursive uses and behavioral habits of francisation, we take an approach inspired by textometry and corpus linguistics. We will question a corpus created from the website of the Ministère de l’Immigration, de la Francisation et de l’Intégration (mifi) of the Government of Québec. After having retraced the fundamental stages of the development of francisation in European and American, French and Quebec terminology databases, we will analyze their semantic behavior by studying these co–occurrences in the corpus chosen on the txm and Hyperbase softwares.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".