Variable Omission of <i>ne</i> in Real-Time French Chat: A Corpus-Driven Comparison of Educational and Non-Educational Contexts
Bibliographic record
Abstract
This article reports on the variable omission of the French negative particle ne (the first marker of verbal negation) in synchronous (i.e., real-time) electronic communication environments. Patterns of variation in a corpus of non-educational chat (i.e., free, public-access Internet chat) are analyzed and compared to data produced by first-, second-, and third-year American university students of French in an educational setting. First- and second-year students retained ne nearly categorically; third-year students used ne five times more often than participants in the non-educational context. Considerable inter-individual variation was observed in the third-year student data, although only one student exhibited native-like patterns of variation. The results are discussed within the broader context of teaching and learning sociolinguistic variation in French as a second or foreign language, with a specific focus on using authentic electronic discourse as one way of sensitizing students to sociolinguistic variation.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".