The Ethnic Group Affiliation and L2 Proficiency Link: Empirical Evidence
Bibliographic record
Abstract
With economic globalisation making second language (L2) learning inevitable throughout the world, understanding what factors facilitate success is a socioeconomic necessity. This paper examined the role of social factors, those related to ethnic group affiliation (EGA), in the development of L2 proficiency. Although numerous studies have documented an intimate relationship between language and EGA, few have examined whether and how this relationship shapes L2 learning. The participants were 59 adult French–English bilinguals from Québec who read an English text and completed a questionnaire assessing their EGA, including pride, loyalty and support for their ethnic group and its language. Results revealed a significant, albeit complex, association between EGA and L2 proficiency. Basic feelings of pride and loyalty towards the ethnic group had no associations with L2 proficiency. Strong support for the group's sociopolitical aspirations were associated with low L2 proficiency. In turn, strong ethnic group identification, coupled with a positive orientation towards the L2 group, was associated with high L2 proficiency. These EGA effects were found to be mediated by amount of L2 use, revealing a plausible link sustaining the relationship between EGA and L2 learning success.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".