A sociolinguistic study of the origins of <i>ne</i> deletion in European and Quebec French
Bibliographic record
Abstract
We present and discuss the results of a sociolinguistic historical study of variable deletion of the preverbal negative particle ne ‘not’, a phenomenon observable in many contemporary varieties of spoken French, but which has not yet made its way into standard written French. Our study's two main goals are (i) to contribute to the resolution of a debate over the point in time when ne deletion became a prevalent feature of nonstandard spoken French, and (ii) to assess the role of the affixal status of subject clitic pronouns in the rise of ne deletion. Our study is based on the analysis of an extensive database comprising a wide range of seventeenth-, eighteenth-, nineteenth-, and early twentieth-century sources providing information on the typical features of nonstandard spoken European and Quebec French. It reveals that ne deletion became widespread in nonstandard spoken French only in the nineteenth century and leads us to hypothesize that the affixal status of subject clitic pronouns contributed to the rise of ne deletion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".