Perfect Timing: Acquisition of the Spanish Present Perfect in a Francophone Context
Bibliographic record
Abstract
Perfect Timing: Acquisition of the Spanish Present Perfect in a Francophone Context Joanne Markle LaMontagne Doctor of Philosophy Department of Spanish and Portuguese University of Toronto 2016 Abstract This thesis explores the possible role of cognate (similar) morphological forms and semantics in a model of cross-linguistic influence in bilingual language acquisition. Much of the previous research on the question of what counts as similarity and overlap in different languages, as a condition on language influence, has focused on syntactic structures (e.g., Hulk Müller, 2000; Strik Pérez-Leroux, 2011; Unsworth, 2003; Yip Matthews, 2000, 2007, 2009). Verbal morphemes and their corresponding semantics, however, have not been investigated before as conditions on language influence. Some of the work on this area has examined the role of shared semantic features related to Tense in bilingual children growing up in a language contact situation in Quechua and Spanish (e.g., Sánchez, 2004). Morphological similarity, however, has not been explicitly tested as to whether it determines language influence. This dissertation investigates whether cognate morphological forms and semantics (i.e., semantic features) are determinants of language influence, and whether quantitative and/or qualitative differences between monolingual and bilingual children occur. I present an experimental study that tests the grammatical knowledge of Spanish tense-aspect-mood and copula selection in Spanish heritage children growing up in a Canadian Francophone context. Four semantic contrasts are tested (e.g., Preterite/Present Perfect, Preterite/Imperfect, Subjunctive/Indicative, and ser/estar), all of which have shown sensitivity to bilingual effects such as language transfer, incomplete acquisition, and attrition. Such effects have been attested in studies on child and adult Spanish heritage language acquisition (e.g., Cuza, 2008, 2010; Cuza Miller, 2015; Miller Cuza, 2013; Montrul, 2002b; Montrul Slabakova, 2002, 2003; Silva-Corvalán, 1994, 2003). In order to examine the subtle, yet important, differences between the Spanish and French tense-aspect-mood systems, a contrastive analysis as in Cowper’s (2005) feature geometry analysis for features of Inflection is adopted. Results from a receptive vocabulary and a sentence imitation task show that while monolingual children outperform heritage children, the latter also show growth and development despite prolonged contact with French. This trend is also confirmed in the contextualized preference-based elicitation task, the main task chosen for this study. Language influence from French to Spanish occurs in heritage children, specifically overextension of certain verbal forms and feature reassembly, but no effect of form similarity (i.e., a cognate boost) is found.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".