Acceptable ungrammaticality in sentence matching
Bibliographic record
Abstract
This article presents a new set of experiments using the sentence-matching paradigm (Forster, 1979; Freedman and Forster, 1985; see also Bley-Vroman and Masterson, 1989), investigating native speakers' and second language (L2) learners' knowledge of constraints on clitic placement in French. Our purpose is three-fold: • to shed more light on the contrasts between native speakers and L2 learners observed in previous experiments, especially Duffield and White (1999), and Duffield et al . (2002); • to address some of the specific criticisms of the sentence-matching paradigm levelled by Gass (2001); and • to provide a firm empirical basis for follow-up experiments with L2 learners. The results reported here provide some confirmation of the validity of Duffield et al.'s earlier work, and help to adjudicate among competing interpretations of the previous effects.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".