Literacies in Times of Crisis: A Trioethnography on Affective and Transgressive Practices
Bibliographic record
Abstract
ABSTRACT Utilizing duoethnography (NORRIS; SAWYER, 2012), the authors explore challenges and opportunities for critical language teaching in times of crisis. Following a brief introduction of research methodology, the authors’ trioethnography dialogically examines three topical areas of particular concern in Brazil and Canada: 1. The potency of affect and its relevance for applied linguistics and language teacher education; 2. The re-emergence of “literacy wars” in education, with attention to their ideological and epistemological interconnections to social power relations; 3. Emerging implications for language and literacy pedagogies in which the authors share classroom experiences and transgressive strategies informed by plurilingual and affective insights. The complexity and variety of settings discussed in this final section help promote the possibilities for critical research and teaching in these difficult and dangerous times.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".