'Anything <i>you</i> can do, <i>tu</i> can do better': <i>tu</i> and <i>vous</i> as substitutes for indefinite <i>on</i> in French
Bibliographic record
Abstract
Research on Montreal French (Laberge and Sankoff 1979; Thibault 1991) has shown a spectacular rise in the use of indefinite tu (or vous) in recent decades, at the expense of the standard form on. Although grammars of French have traditionally passed over indefinite tu/vous in silence, Ashby's study of Tours French (1992) confirmed that the phenomenon exists in metropolitan French also. The historical time‐depth of indefinite tu/vous has apparently not been explored previously, though Posner (1997) has suggested that indefinite tu is a modern feature, found especially in Canada. A survey of indefinite tu/vous in earlier periods and in a range of varieties forms the first part of this paper. Secondly, drawing on a corpus of French spoken in Picardy, northern France, the paper investigates the extent to which this use of the 2nd person pronouns: (i) helps to avoid ambiguity; (ii) co‐occurs with another grammatical variable. Unlike the surveys of Montreal and Tours, the Picardy corpus includes a large majority of informants who used tu to address the interviewer, and this too is explored as a potential influence on speakers’ use of 2nd person pronouns with indefinite reference.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| 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".