Participation in literature and content subject classes: Culture, ethnicity and social space(s)
Bibliographic record
Abstract
This qualitative case study discusses six English Language learner (ELL) adolescents' experiences of language use in ESL classes, English Literature classes and content subject classes (i.e., Math and Science) in high schools of Toronto, Canada. We found that participants perceived English Literature classes as a social space of “others” where they were more likely to keep silent for several reasons. In contrast, ESL classes and content subject classes were considered as a social space of “ours” within which they participated more actively with hybrid forms of language use and sociocultural practice. This article links the findings to the nature of social spaces and language use. In particular, the content and interaction in classroom activities are explored, which form multiple social spaces for language use and impacts learning outcomes. The study concludes with a discussion on content, interaction and local practice in the school curriculum to enhance second language learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".