Code Switching Use, Attitudes, and Identity: Differences Among Spanish-English Bilinguals in Canada, Mexico, and the United States
Bibliographic record
Abstract
Code-switching (CS) has been extensively studied for a variety of purposes and under many contexts. In recent years there has been a shift in CS literature to better understand the sociological forces that affect speakers’ use of CS. While in earlier literature, CS was perceived negatively by both speakers and the general public (Milroy & Muysken, 1995; MacGregor-Mendoza, 2021; Anderson & Toribio, 2007; Fishman, 1967), it has since been shown that many bilinguals view CS positively. More recent research suggests that bilinguals perceive CS as an important part of their identity and use it to show they belong to particular groups (Yim & Clément, 2021; Rothman & Rell, 2005; Duff, 2012; Buchlotz & Hall, 2005; Bustamante-López, 2008; Torrez, 2013; Norton, 1997; Norton, 2013). These recent studies regarding CS and attitudes have largely focused on individual differences (Dewaele & Wei, 2014a; Gardner-Chloros, McEntee-Atalianis, & Finnis, 2005; Moses et al., 2021; Peña-Díaz, 2004; Urciuoli, 2014; Montes-Alcalá, 2009; Chappell & Faldis, 2007; Yim & Clément, 2021). In this research, I posit that the country in which bilinguals live influences their attitudes toward CS use due to differences in immigration policies in each country. Considering that the three countries have different attitudes toward immigrants (Brosseau & Dewing, 2018; Environics Institute of Survey Research; The Gallup Organization, 2022; Budiman, 2020; Sief & Clement, 2019), this could have an impact on how immigrants themselves use CS and their attitudes toward it. Spanish-English and English-Spanish bilinguals in three countries (Canada, the United States, and Mexico) took a survey that evaluated their attitudes toward CS, frequency of use, and if and how they used CS to form their identity. The results of the study suggest that there are differences in attitudes about CS between bilinguals in these three countries. Moreover, the results demonstrated that while Canadian bilinguals had more positive feelings overall toward CS, bilinguals in the U.S. used CS more often.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".