The Facilitative Role of L1 Influence in Tense–Aspect Marking: A Comparison of Hispanophone and Anglophone Learners of French
Bibliographic record
Abstract
English learners of French whose first language (L1) does not mark the perfective/imperfective distinction have shown verb semantic influence and an overall preference for perfective over imperfective in their use of second language (L2) tense–aspect markers. This study investigated whether learners whose L1 marks the perfective/imperfective distinction would exhibit similar acquisition profiles. Hispanophones (n= 17) and Anglophones (n= 15) at similar levels of French L2 proficiency completed a 68‐item cloze task with equal numbers of perfective and imperfective contexts distributed across 4 semantic categories: stative, activity, accomplishment, and achievements. In a 20‐minute retrospective interview, a subsample of the participants (8 Hispanophones, 11 Anglophones) commented on factors influencing their tense–aspect choices. An ANOVA of 1,012 predicates revealed that unlike the Anglophones, the Hispanophones did not prefer perfective over imperfective, and they were also less influenced by verb semantics. The learners' comments suggest that the Hispanophones made effective use of L1–L2 similarities, whereas the Anglophones appealed to verb semantics and partially understood pedagogical rules, which were frequently associated with inappropriate uses of the forms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".