Bibliographic record
Abstract
In this paper, the authors propose that attention to affect/emotion be given greater prominence in applied linguistics following a theoretical and pedagogical framework delineated as critical affective literacy (CAL) by Anwaruddin (2016). Following the IICOLA conference theme of emotions in multidisciplinary studies, the authors outline the interdisciplinary influences (e.g., philosophy, memory studies, semiotics/multimodality, citizenship education, etc.) that underpin key CAL principles and their understanding of affect/emotion in applied linguistics. In support, the authors discuss the potency of affect and emotionality of texts by way of duoethnography (Norris & Sawyer, 2012, 2017), a research methodology they have utilized in exploring affective/emotional dimensions of language in educational domains (e.g., English for Academic Purposes and Language Teacher Education) and as part of broader socio-political deliberation (i.e., critical citizenship pedagogies). The authors detail specific features of duoethnographic research methodology (e.g., participant transparency and juxtaposition, epistemological and ideological risk-taking) that contribute to CAL principles and aspirations. The authors also identify several implications of their work for the development of CAL in applied linguistics followed by brief descriptions of curricular and pedagogical innovations where affect/emotion have been integral to the pedagogical and literacy strategies described.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".