A systems approach to multilingual language attitudes: A case study of Montréal, Québec, Canada
Bibliographic record
Abstract
Purpose: People are shaped holistically by dynamic and interrelated individual and social-ecological systems. This perspective has been discussed in the context of varied aspects of bilingual experiences, namely language acquisition and development. Here, we applied a Systems Framework of Bilingualism to language attitudes, which may be especially responsive to social-ecological influences. Methodology: One hundred twenty-three French–English bilingual adults ( M age = 21.20, SD = 3.21) completed self-report questionnaires on demographic information and their attitudes toward languages. A subset of these bilinguals ( n = 73) completed a social network survey. Data and analysis: We used language-tagged social network analysis and geospatial demographic analysis to examine the role of individual characteristics (i.e., first language), interpersonal language dynamics (i.e., person-to-person interactions), and ecological language dynamics (i.e., neighborhood language exposure). Findings and Conclusions: At an individual level, we found that bilinguals’ language background (i.e., first language) predicted attitudes of solidarity toward a language (i.e., whether a language is associated with personal identity and belongingness). When considering sociolinguistic layers of influence, we found that bilinguals’ social network and neighborhood-level language exposure jointly predicted their attitudes of solidarity toward a language, as well as their attitudes toward the protection of minority languages. Originality: While most studies have examined language experience in a unidimensional nature, the present study investigated multilingual language attitudes by considering multiple systems within a social-ecological framework. Implications: Taken together, the results suggest that several interrelated interpersonal and ecological systems are associated with language attitudes, which could have important implications for planning future language policies in multilingual societies such as Montréal.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".