The embedded <i>Wh- in situ</i> clause: French tout court? <i>Les spasmes musculaires incontrôlés, je sais pas c’est quoi</i> (France Info 20/10/21)
Bibliographic record
Abstract
Abstract Although not studied by the generative approach (Shlonsky 2017), the embedded Wh - in situ clause nevertheless belongs to the French language spoken in a large amount of areas, and, we believe, to “français tout court” (Blanche-Benveniste & Jeanjean 1987): until now, it has mainly been studied in Quebec (Lefebvre & Maisonneuve 1982; Blondeau & Ledegen 2021) and in Reunion Island (Ledegen 2007a, 2007b, 2007c, 2016; Ledegen & Martin 2020), but it has recently been massively attested in the Multicultural Paris French project in the suburbs of Paris (Gardner-Chloros & Secova 2018) and Strasbourg (Marchessou 2018). These new data could be read as a language contact, as a recent linguistic change, or as a long-established “popular” structure (Guiraud 1966), different analytical hypotheses that will be detailed in this study. These recent data also argue in favour of the methodology of ecological corpora, obtained within the framework of a strong acquaintanceship and located at the pole of communicative proximity (Koch & Oesterreicher 2001). The examination of various existing corpora will reveal the structural functioning of the structure and contrast the corpora following the modes of interaction, the oral or written medium, as well as on the chronological axis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".