The evolving grammar of the French subjunctive
Bibliographic record
Abstract
Abstract This paper compares the evolution and contemporary distribution of subjunctive and indicative in spoken Quebec French with the development of normative injunctions on variant choice over five centuries of grammatical tradition. The subjunctive has been prescribed with hundreds of lexical governors, verb classes and semantic readings since the 16th century, but in spontaneous speech, it is virtually limited to a handful of matrix and embedded verbs. Our analysis shows that the overriding determinant of variant choice is not meaning, as most would claim, but the lexical identity of the governor. The only other factors that play a role are those pertaining to the construal of the context as canonical for subjunctive (e.g. suppletive morphology, presence of the complementizer que, and adjacency of main to embedded clause); where these are present, subjunctive is favored. Quantitative discrepancies among governors and embedded verbs, their previously undocumented associations (or lack thereof) with the subjunctive, and the unpredictable mood preferences they display at different points in time have all conspired in obscuring community patterns. Once actual usage facts are systematically analyzed, however, the grammar of subjunctive selection emerges as regular and stable. Its discrepancies with respect to both normative and theoretical linguistic accounts stem from attempts to impose the doctrine of form-function symmetry on a phenomenon which is inherently variable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".