Examining Gender Differences In Heritage Language Maintenance And Loss Among South Asian First-Generation Canadians, And The Effect On Well-Being
Bibliographic record
Abstract
Language is prevalent in various aspects of life. Not only is it often essential for communication, but it also has deep roots within cultural contexts. Specifically, within South Asian culture knowing one’s heritage language is considered to be necessary when it comes to connecting with one's cultural group and identity. Heritage language maintenance can be seen differently within different individuals and have different implications for them. This study was designed to explore gender differences in heritage language loss as well as the role language may play in one’s well-being. Canadian-born South Asian children and young adults aged 10-25 completed an online survey which looked at demographics, acculturation/enculturation, bilingual dominance, well-being, as well as language skills. Following the survey participants met the researcher over zoom to complete a vocabulary test. The study aimed to answer the questions of whether there are gender differences in heritage language loss/maintenance in second generation South Asian immigrants, as well as whether being proficient in one's heritage language affects their well-being. There was no significant relationship found between gender and language skill, except when looking at the association between gender and English vocabulary scores. However, gender was found to be a predictor of English vocabulary test scores. In addition, there was no relationship found between well-being and language skill, nor did well-being predict language skill. This research aids in providing important insight into the impact language has on individuals within certain communities both at a group and individual level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".