Literacies in Times of Crisis: A Trioethnography on Affective and Transgressive Practices / Letramentos em Tempos de Crise: Uma Trioetnografia de Afetos e Práticas Transgressivas
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.KEYWORDS: language education; critical literacies and pedagogies; affect; duoethnography.RESUMO: Utilizando a duoetnografia (NORRIS; SAWYER, 2012), os autores exploram desafios e oportunidades para o ensino crítico de línguas em tempos de crise. Após uma breve introdução à metodologia de pesquisa, a trioetnografia utilizada pelos autores examina dialogicamente três áreas temáticas de particular interesse no Brasil e no Canadá: 1. A potência do afeto e sua relevância para a linguística aplicada e a formação de professores de línguas; 2. O ressurgimento das “guerras de letramentos” na educação, no que diz respeito às suas interconexões ideológicas e epistemológicas com as relações sociais de poder; 3. Implicações emergentes para práticas de ensino de língua e de letramentos, por meio das quais os autores compartilham experiências de sala de aula e estratégias transgressivas informadas por abordagens plurilíngues e afetivas. A complexidade e variedade de aspectos discutidos neste artigo ajudam a promover as possibilidades de pesquisa e ensino críticos nestes tempos difíceis e perigosos.PALAVRAS-CHAVE: educação linguística; letramentos e pedagogias críticos; afeto; duoetnografia.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".