Acquisition of English Tense‐Aspect Morphology by Advanced French Instructed Learners
Bibliographic record
Abstract
The acquisition of English verbal morphology has been mostly tested as a second language (L2) in English-speaking settings (Bardovi-Harlig, 1992a, 1992b, 1992c, 1998; Bardovi-Harlig & Bergström, 1996; Bayley, 1991, 1994), more rarely as a foreign language (e.g., Robison, 1990, 1995), in only one cross-sectional study with native speakers of French in a foreign/L2 setting in Quebec (Collins, 2002), and never with French speakers living in France, who have much less exposure to English than their Francophone counterparts living in Quebec. The present cross-sectional study analyzes data from a group of 21 high school French speakers learning English in France to address two main research questions: (a) Do our learners exhibit nativelike performance in their use of the various past morphological forms across the lexical aspectual classes (e.g., Vendler, 1957/1967)? (b) Does their first language lead French speakers to overuse the English present perfect due to its morphological similarity with the passé composé? Our findings underscore the effect of lexical aspect on the use of past tense markers while highlighting a significant departure from the predicted developmental path of past tense marking: States are marked more consistently than telic events in the narrative task. Possible theoretical and methodological factors that might account for the present findings are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".