Group membership and identity issues in second language learning
Bibliographic record
Abstract
Language learning is inextricably linked to a social context, and this implies that context-related social variables, such as ethnicity or attitudes, can influence how language learning unfolds. Among the many group-engendered social factors, ethnic identity appears to have interesting consequences for language teaching and learning (Pavlenko & Blackledge 2004). Indeed, issues of personal and group identity often become important when individuals or groups come in contact with one another to learn a language. Briefly, ethnic identity refers to a person's subjective experience of being a part of an ethnic group (Ashmore, Deaux & McLaughlin-Volpe 2004). For second language (L2) learners, the two relevant groups are usually their primary (home) ethnic group and the L2 community. We report here on the research that we have been conducting at Concordia University in Montreal, as part of the Centre for the Study of Learning and Performance, with the goal of investigating the role of ethnic group identity in L2 learning.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".