Mood Selection in Relative Clauses by French–Spanish Bilinguals: Contrasts and Similarities between L2 and Heritage Speakers
Bibliographic record
Abstract
In this paper, we explore three issues related to the acquisition of mood selection in Spanish relative clauses by second language (L2) and heritage (HL) speakers of Spanish: (1) whether HL speakers are more native-like than L2 learners; (2) whether the speakers’ performance differs depending on task modality (written vs. oral), since HL speakers are known to perform better in oral tasks and L2 learners tend to do better in written tasks; and (3) whether knowledge of French as an L1/dominant language (DL) has an impact on the acquisition of Spanish subjunctive, since both languages include this mood in their grammars, but it is used more productively in Spanish. Results from a sentence combination felicity task (SCFT) in Spanish—in written and oral forms—and a written elicited production task (EPT) in French, administered to advanced L2 and HL speakers of Spanish whose L1/DL is French and two monolingual (Spanish and French) control groups, revealed that L2 learners pattern more closely with the control group than HL speakers in the SCFT, both written and orally. In the EPT, all bilingual speakers display higher levels of subjunctive use than the control group, showing a potential influence from the L2/weaker language on the L1/DL.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".