The morphophonological dimensions of Spanish gender marking: NP processing in Spanish bilinguals
Bibliographic record
Abstract
The processing literature provides some evidence that heritage Spanish speakers process gender like monolinguals, since gender-marking in definite articles facilitates their lexical access to nouns, albeit these effects may be reduced relative to speakers who learned the language as majority language. However, previous studies rely on slowed-down speech, which leaves open the question of how processing occurs under normal conditions. Using naturalistic speech, our study tests bilingual processing of gender in determiners, and in word-final gender vowels. Participants were 17 adult heritage speakers of Spanish (HSSs) and 21 adult Spanish-speaking immigrants (ASIs). We presented these bilinguals with questions containing either a definite article or an unmarked possessive (¿Dónde está la/mi pala? ‘Where is the/my shovel?’) in a three-object display. Gaze fixations were recorded during determiner, noun and post speech processing. Nouns were controlled for gender, morphological transparency, gender alternation, and animacy. Individually, heritage speakers tend to fall within the performance range of adult immigrants, but statistical analyses show that ASIs have more fixations to targets for definite articles compared to HSSs. For HSSs the advantage of gender-marking appears later, during noun processing. In contexts where the noun-final vowels were the only cue to lexical selection, HSS had less looks to targets with alternating nouns, and with feminine nouns. When presented with natural speech, despite the great overlap between adult immigrant and heritage speakers, there are quantitative differences in how HSS process gender both for syntactic agreement (gender in articles) and noun morphophonology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".