Variable Subject Pronoun Expression in the Spanish of Londombia: A study of language contact in Canada
Bibliographic record
Abstract
According to the extended projection principle, subjects are mandatory in tensed clauses (Chomsky 1982). Languages, however, vary in their use of null and overt subjects. Languages like English, which are [-null subject] languages, require overt subjects (1a), rendering phrases with null subjects ungrammatical (1b), while languages like Spanish, which are [+ null subject] languages, allow for both overt (2a) and null subjects (2b).\n(1) a. She wants bread.\nb. *Ø wants bread.\n(2) a. Ella quiere pan.\nb. Ø quiere pan.\n“(She) wants bread”\nIn Spanish the variable use of Spanish subject personal pronouns (SPPs) has been studied in monolingual (Cameron 1992; Orozco 2015) and bilingual populations (Otheguy et al. 2007, Montrul 2004), and studies have shown that the rate of use of null vs. overt subject pronouns varies between different varieties of Spanish. In bilingual populations, an increase in use of overt SPPs has been documented in some populations (Otheguy et al. 2007). However, it is debated whether this is due to contact with English, a [- null subject] language, or with other varieties of Spanish which show a higher rate of use of overt SPPs such as Caribbean varieties of Spanish (Flores-Ferrán 2004).\nIn this dissertation, the results of an investigation regarding the variable use of SPPs in two generations of Colombian Spanish speakers (N(1Gen)=10, N(2Gen)=10) living in London, Ontario are reported. A total of 2366 tokens from 20 sociolinguistic interviews are used to calculate frequency of use of overt SPPs for each generation, and to determine the social (generation, age, gender, and interview modality), and linguistic factors (pronoun person and number, switch reference, semantic verb type, clause negation, position of pronoun in relation to verb, verb tense, verb mood, and clause type) that condition variable use of SPPs in this population. In addition, this study adopts an embedded mixed-methods approach by also considering the data from a qualitative perspective to examine whether the attitudes, language use habits, and ties to cultural identity of Colombian speakers align with factors known to favour heritage language maintenance across generations. This approach also provides valuable contextual information for the quantitative analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".