Auxiliary Selection in 16th Century French: Imposing Norms in the Face of Language Change
Bibliographic record
Abstract
Auxiliary selection- the choice of etre or avoir - in the conjugation of certain intransitive verbs (i.e. tomber) in French has garnered limited attention in the linguistic literature. On the rare occasion when the topic is addressed, authors usually take one of two approaches. The majority of studies on auxiliary selection in French focus on a description of auxiliary use in modern, regional dialects of French, such as Canale et al. (1978) on Ontario French, Sankoff and Thibault (1980) on Montreal French, Russo and Roberts (1999) on Vermont French, Willis (2000) on Ottawa-Hull French and Balcom (2008) on New Brunswick Acadian French. The other approach focuses on the “Unaccusative Hypothesis” (Legendre and Sorace 2003, Bentley and Eythorsson 2003). This hypothesis is applied to a cross section of European languages with French sometimes being cited as one language example among others. What is even more rarely addressed, and what is vitally missing in the linguistic literature, is auxiliary selection as seen from a historical perspective. Historical information on auxiliary selection is not totally nonexistent, but no comprehensive study of the historical nature of auxiliary selection has been done for French. Willis (2000) did address this question briefly, but his main focus was on the contemporary Ottawa-Hull regional dialect so the scope of his historical information is limited. It includes some references for the 17th century, but focuses mainly on the 18th century. Tailleur (2007) studied auxiliary selection in 18th century French, but her study was not a description of auxiliary use in this time period. Her objective, rather, was the application of the “Unaccusative Hypothesis” to 18th century French. Other sources of historical information on auxiliary selection in French are found in works dealing with the history of the French language (i.e. Fournier 1998, Tritter 1999). These works do provide some information on auxiliary selection in a historical context, but again this grammatical point, when addressed, remains very limited. In this article, I propose to start filling in this linguistic gap by looking at auxiliary selection in 16th century French. More precisely, I will look at I) the state of auxiliary selection - whether it was stable or in transition, and II) how auxiliary selection was perceived and analysed by early grammarians in their first efforts to standardise the French language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".