Stylistic and discursive functions of French negative particle<i>ne</i>in an educational context
Bibliographic record
Abstract
This study examines use/non‐use of negative particleneby students and teachers in the high schools of four Ontario Francophone communities. The students were recorded in semi‐directed interviews and in the classroom. The teachers recorded themselves in the classroom. In the interviews,neuse is marginal and its non‐use ubiquitous and not influenced by social class, gender or topic formality. Overall, students do not usenesignificantly more in the classroom than in the interviews. These findings do not support the hypothesis in some studies thatneuse is a hyper‐stylistic variant, which hinged on the prescription ofneuse in writing. In the classroom, however, teachers display markedly different levels ofneuse/non‐use according to subject taught, speaker age, and addressee/discourse functions performed. Thus, there is some evidence thatneuse/non‐use is not entirely devoid of stylistic markedness, at least for teachers in the formal setting of the classroom. Finally, an examination ofneuse/non‐use co‐occurring with two phonological variants of post‐verbal negatorplusreveals findings that are germane to the debate concerning the possibility of analysing stylistic variation in spoken French as a form of diglossic code‐switching.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".