Word order in French: the role of animacy
Bibliographic record
Abstract
A major goal of the quantitative study of syntax has been to identify factors that have predictive power on speaker choices in the face of word-order or valence alternations (e.g. Arnold et al. 2000; Bresnan et al. 2007; Bresnan & Ford 2010; Bader & Häussler 2010). In this paper, we study the role of animacy on the order of constituents in French. Animacy has been shown to affect sentence production in other languages, either directly (Feleki & Branigan 1999; Kempen & Harbusch 2004; Tanaka et al. 2011) or indirectly through grammatical role assignment (McDonald et al. 1993). Corpus studies however, have failed to find such an effect in French (Thuilier 2012a; Thuilier et al. 2014). Using a sentence recall task, we examined whether animacy has an impact on linear ordering or on grammatical function assignment. While we do find evidence for a role of animacy in the choice between active and passive voice, we do not find a preference to place animate arguments first with ditransitive verbs nor with nominal coordinations. While these findings tend to support the indirect hypothesis (McDonald et al. 1993; Kempen & Harbusch 2004), we also find what may look like an anti-animacy effect: inanimate direct objects tend to precede animate indirect objects. We propose that canonical mappings between syntactic function and semantic role play a role in putting (inanimate theme) direct objects before (animate recipient) indirect objects, thus overriding the animacy first tendency in French.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".