Eventive and stative passives in Spanish L2 acquisition: A matter of aspect
Bibliographic record
Abstract
This paper reports on an empirical study that examined knowledge of eventive and stative passives in the L2 Spanish grammar of L1 speakers of English. Although the two types of passive exist in English, the difference between them is not signaled in any specific way. In Spanish, in contrast, the distinction is marked by the choice of copula:seris used to form eventive passives,estarfor statives. Researchers agree that the two copulas, both of which translate as English “to be”, differ in relation to aspect:estaris perfective while ser is not marked for aspect (Schmitt, 1992). The question was whether L2 learners would be able to acquire the aspectual difference of the copulas and apply it to the formation of the passives. Two main tests were used, a Grammaticality Judgment Task and a Sentence Selection Task. The Grammaticality Judgment Task examined properties of the passives related, among other things, to aspect and agentivity. The Sentence Selection Task focused on the interpretation of the subject: only the subject of ser can be interpreted as generic. Although the learners in general distinguished between grammatical and ungrammatical sentences, they had not acquired the restriction on subject interpretation. These results are explained in terms of interfaces.
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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 | 0.002 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".