Bibliographic record
Abstract
This paper addresses the contribution that corpus-based studies of syntactic variation can make to the construction, elaboration and testing of formal syntactic theories, with a particular focus on the testing dimension. In particular, I present a new empirical study of obligatory and optional asymmetric negative concord phenomena, and I show how an influential analysis for obligatory concord patterns (de Swart, 2010) can be tested using variation data through looking at the predictions that its natural probabilistic extension makes for the forms, interpretations and frequency distributions of expressions in languages in which asymmetric concord is optional. In obligatory negative concord languages like Spanish, negative indefinites, such as nadie ‘no one’, appear bare in preverbal position (i.e. in an expression like Nadie ha venido ‘No one came’), but they co-occur with the negative marker no in postverbal negative concord structures such as No he visto a nadie ‘I did not see anyone.’ (lit. ‘I did not see no one.’). Furthermore, in this language, co-occurrence between a negative marker and an n-word is either prohibited (*Nadie no ha venido), or it is obligatory (*He visto a nadie). Québec French shows a variable version of the Spanish pattern in which the negation marker optionally co-occurs with postverbal negative indefinites (J’ai (pas) vu personne ‘I saw no one’) but is prohibited with preverbal negative indefinites *Personne est pas venu (Ok: Personne est venu. ‘No one came’). I show how the predictions for Montréal French of de Swart’s analysis of Spanish can be tested (and, in this case, mostly verified) using a quantitative study of the distribution of bare and concord structures in the Montréal 84 corpus of spoken Montréal French (Thibault & Vincent, 1990) through looking at its natural extension within Boersma (1998)’s stochastic generalization of the Optimality Theory framework, which is the framework in which de Swart’s proposal is set.
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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".