Learner beliefs about sociolinguistic competence: A qualitative case study of four university second language learners
Bibliographic record
Abstract
Abstract This article explores the beliefs about second language (L2) sociolinguistic competence of four university-level advanced L2 learners. It places particular emphasis on 1) how these university learners conceptualized L2 sociolinguistic competence; 2) how they thought about two different language learning contexts (viz., the L2 classroom versus study abroad) for their development of sociolinguistic competence; and 3) the L2 strategies they employed to develop their L2 sociolinguistic knowledge. The study found, first, that the university learners conceived of sociolinguistic competence as the ability to adjust their language to reflect the social distance between themselves and other speakers. Second, it demonstrates that the university learners believed that the classroom environment might provide them with insufficient opportunities to utilize those L2 registers that are most sensitive to context. Nevertheless, their beliefs about the necessity of having sociolinguistic competence played a pivotal role in helping most of them to find meaningful L2 learning opportunities in the classroom, as well as in study-abroad contexts. Finally, while immersive experiences can help learners sustain L2 motivational strategies according to their ultimate L2-related goals, the present study lends support to the observation that contexts per se cannot function as meaningful L2-rich environments.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".