Teaching the Nation(s): A Duoethnography on Affect and Citizenship in a Content‐Based<scp>EAP</scp>Program
Bibliographic record
Abstract
Abstract The plurality of nation in this title foregrounds the challenge of teaching a geopolitical entity whose survival depends on building emotional ties of belonging. These ties can be problematic in diverse societies in which collective identities compete for recognition. In Canada, nationhood tied to language and culture is claimed by French‐speaking Quebecers; it is also invoked by many Western‐Canadian politicians to express a growing alienation from Eastern Canada's perceived socio‐economic dominance. In Canada's constitution, the term First Nations represents the indigenous peoples who are the country's original inhabitants. In this context, teaching the nation(s) is indeed challenging. In response, the authors adopt duoethnography as both research methodology and pedagogy in their content‐based English for Academic Purposes (EAP) courses. They first explore their experiences and emotional attachments to nationhood, reflecting on their influences on teaching around language and citizenship. They then provide two EAP assignments as examples: The first is a course assignment in which students critically examine hyphenated national identities through duoethnographic inquiry. The second is called the Get Involved project, which examines service learning and citizenship. Both examples demonstrate the importance of critical affective literacies to expand the pedagogical repertoires of EAP teachers and students in a time of resurgent nationalism.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".