Bibliographic record
Abstract
Duoethnography is an emerging methodology in English language teaching (ELT)/applied linguistics where two or more participants critically examine a shared phenomenon or experience as a way to challenge assumptions and develop new understandings of critical events (Lowe & Lawrence, 2020). It is a flexible tool with an emphasis on interaction, both between people, and people and various physical or digital artefacts (e.g., documents, academic literature). In this paper, we outline our duoethnography on our experience with the Certificate for English Language Teaching to Adults (CELTA) with a focus on how academic literature and social media pervaded our inquiry. We highlight how academic articles and social media were used as artefacts in our study and how their role as “co-participants” enhanced our investigation. La duoethnographie est une méthodologie émergeante dans l’enseignement de l’anglais (ELT)/en linguistique appliquée dans laquelle deux participants ou plus examinent de façon critique un phénomène ou une expérience partagée comme manière de remettre en question les hypothèses et de créer de nouvelles voies pour comprendre des évènements critiques (Lowe & Lawrence, 2020). Il s’agit d’un outil souple qui met l’emphase sur l’interaction, à la fois entre les personnes, ainsi qu’entre les personnes et divers artefacts physiques ou numériques (par exemple, des documents, des écrits universitaires). Dans cet article, nous exposons notre duoethnographie dans notre expérience du Certificat pour l’enseignement de l’anglais aux adultes (CELTA), en portant une attention particulière à la façon dont les écrits universitaires et les médias sociaux ont imprégné notre enquête. Nous soulignons la manière dont les articles universitaires et les médias sociaux ont été utilisés comme artefacts dans notre étude et de quelle façon leur rôle de « coparticipants » a mis notre enquête en valeur.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.003 |
| 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".