Bibliographic record
Abstract
Abstract Responding to observations in ELF research that Anglophone-centric attitudes towards English are eroding among speakers of a younger generation, this paper demonstrates that attitudes towards English can in fact vary among youth of different social class backgrounds. Drawing on a case study of immigrant Filipino adolescents in Vancouver, this paper examines how class differences of these youth impinge on their lived experiences and the material conditions of their migration, shaping how they negotiate their linguistic capital, particularly their use of English. Data illustrate how such conditions shape their dispositions, their sense of agency, and feelings of linguistic confidence and insecurity. Using Darvin and Norton’s (2015. Identity and a model of investment in applied linguistics.Annual Review of Applied Linguistics35. 36–56) model of investment as a lens to investigate the interplay of identity, capital, and ideology in communicative contexts, this paper asserts how contrasting language attitudes are constructed by asymmetrical relations of power between speakers. In the spirit of accommodation and adaptation that characterizes ELF communication, this paper calls for a critical pedagogy that enables speakers to reflect on how they position themselves and others in these contexts, and assert their place as legitimate speakers of English.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".