Grammatical Optionality and Variability in Bilingualism: How Spanish-English Bilinguals Limit Clitic-climbing
Bibliographic record
Abstract
This thesis considers how different groups of Spanish speakers (monolinguals, early bilinguals and late bilinguals) organize and limit grammatical optionality related to the placement of Spanish pronominal clitics with many complex infinitival constructions (Spanish clitic-climbing). In examining empirical work on the process and outcome of early and late dual language exposure and how early and late bilinguals acquire and limit grammatical optionality, this study will contribute to our understanding of 1) the nature of language-related cognition at different ages; 2) the systematic nature of bilingual language behaviour in child and adults (transfer, cross-language influence, etc.); 3) the cognitive and contextual factors associated with age of exposure to bilingualism to explain bilingual language behaviour; and 4) the importance of incorporating a clear model of language variation (language-internally and cross-linguistically) into a formal model of (bilingual) language. \n\tThe empirical study conducted here tested how highly proficient heritage speakers (HS) of Spanish (native speakers of Spanish and Spanish-English bilinguals) deal with the optionality of clitic-climbing structures compared to monolingual speakers (native speakers) and highly proficient adult L2 speakers of Spanish (Spanish-English bilinguals). Forty participants completed a picture elicitation task testing a lexical limitation of the optionality, and an acceptability-preference task testing the speakers’ judgments on structural, semantic and lexical limitations of the optionality. Results show that all groups of speakers exhibited knowledge of syntactic constraints associated with pronominal placement in Spanish (optional clitic-climbing) infinitival sentences. All groups also performed similarly in exhibiting sensitivity to non-categorical factors that have been shown to guide the preferences of monolingual Spanish speakers. However, in the production task, the heritage speakers significantly outperformed the monolingual and non-native speakers of Spanish in their use of the Spanish-specific variant (proclisis). I explain these results through both cognitive and contextual factors related to age of exposure to bilingualism, and I discuss how the production results may underestimate a monolingual-bilingual difference for this optional domain.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".