Assessing Differences and Similarities between Instructed Heritage Language Learners and L2 Learners in Their Knowledge of Spanish Tense-Aspect and Mood (TAM) Morphology
Bibliographic record
Abstract
The acquisition of the aspectual difference between the preterit and imperfect in the past tense and the acquisition of the contrast between subjunctive and indicative mood are classic problem areas in second language (L2) acquisition of Spanish by English-speaking learners (Collentine, 1995, 1998, 2003; Salaberry, 1999; Slabakova & Montrul, 2002; Terrell, Baycroft & Perrone, 1987). Similarly, Spanish heritage speakers in the U.S exhibit simplification of the preterit/imperfect contrast and incomplete acquisition/attrition of subjunctive morphology (Merino, 1983; Montrul, 2002, 2007; Potowski, Jegerski & Morgan-Short, 2009; Silva-Corvalán, 1994). This raises the question of whether the linguistic knowledge of a developing L2 learner is similar to incomplete L1 acquisition in heritage language (HL) learners. Because heritage speakers are exposed to the heritage language from infancy whereas L2 learners begin exposure much later, Au et al. (2002, 2008) have claimed that heritage speakers are linguistically superior to L2 learners only in phonology but not in morphosyntax. The present study reexamines this claim by focusing on the interpretation of tense, aspect and mood (TAM) morphology in 60 instructed HL learners and 60 L2 learners ranging from low to advanced proficiency in Spanish. Results of four written tasks showed differences between the groups both in tense and aspect and in mood morphology, depending on proficiency levels. Implications of these findings for heritage language instruction 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".