Negotiating the "critical" in a Canadian English for Academic Purposes program
Bibliographic record
Abstract
This thesis represents a one-year critical ethnographic case study of an academic literacy program located within a major Canadian university. Pacific University's English for Academic Purposes program distinguished itself from "traditional" English as a Second Language programs in its innovative pedagogical approach. The program staff believed that the understanding of a language lies in the deeper understandings of the culture in which it is embedded. Because of this, the program emphasized the use of a critical dialogic approach to the analysis of how language is shaped by culture and vice-versa. My research revealed, however, that disjunctions existed between the pedagogy as it was conceptualized and the classroom practices of the instructors teaching there. Furthermore, classroom observations conducted over the course of the year suggested that student identities were being constructed and negotiated vis-à-vis those of the instructors and that the discourses of teachers essentialized culture and, in turn, student identities. I argue that the discourses we co-construct in the classroom can (re)create subordinate student identities, thereby limiting students' access not only to language-learning opportunities, but to other more powerful identities. I therefore propose that a reimagining of a critical language teacher identity and the negotiation of critical praxis can concomitantly serve to reimagine student identities in new and emancipatory ways.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.063 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".