THE ROLE OF SEMANTIC TRANSFER IN CLITIC DROP AMONG SIMULTANEOUS AND SEQUENTIAL CHINESE-SPANISH BILINGUALS
Bibliographic record
Abstract
This study examines the acquisition of the featural constraints on clitic and null distribution in Spanish among simultaneous and sequential Chinese-Spanish bilinguals from Peru. A truth value judgment task targeted the referential meaning of null objects in a negation context. Objects were elicited via two clitic elicitation tasks that targeted anaphoric contexts and left-dislocated topics. An acceptability task tested sensitivity to left-dislocated object drop. Although simultaneous bilinguals were mostly undistinguishable from monolinguals, the late learners differed from both of these groups across tasks. Age of arrival led to different outcomes, with late learners showing more deficits than the child learners. Late learners avoided using clitics and relied on lexical and null objects. Residual transfer effects were observed among the child learners in the form of insensitivity to the features that serve as the basis for null argument identification and clitic deficits in production. It is also argued that transfer persists despite early and intense exposure to the second language in a natural environment because of the existence of an unmarked argument identification option in the first language.
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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 | 0.000 | 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".