Bibliographic record
Abstract
Today French speakers have access to at least five different variants of wh questions, which shows the inherent complexity of the wh interrogative system. This article aims at providing new pieces of evidence for the analysis of French wh interrogatives. Through the study of the evolution and usage of all variants, we show that the wh+ est-ce que variants are at the center of the system’s evolution. They explain why so many variants have been preserved, and they are also the trigger of a possible structural change affecting the interrogative left periphery. Assuming the diglossic approach, some registers of French lack wh movement, and now produce wh interrogatives that are similar to syntactic clefting. Such an analysis predicts that wh in situ is to be expected, since it is part of the same register – grammar – as the wh est-ce que . This article provides ample empirical evidence, and it represents a first step towards a cleft-like analysis of French wh interrogatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.028 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".