Language Teacher Identity and the Domestication of Dissent: An Exploratory Account
Bibliographic record
Abstract
In this article, the notion of dissent refers to a more critical, ideological orientation to advocacy for and by TESOL professionals. The notion of domestication refers to identity‐forming practices in the knowledge base of language teacher education (LTE) and in professional certification processes that potentially displace this critical orientation. After a discussion of field‐internal examples (e.g., epistemic dependencies, Kumaravadivelu, 2012; linguistics applied, Widdowson, 1980; language objectification, Reagan, 2004), the article takes up a specific context of domestication: TESL Ontario's accreditation processes and requirements for the certification of adult instructors of ESL (English as a second language). Examining organizational documents and membership survey data, the article suggests that the framing of advocacy is inadequate for the conditions of underemployment and overqualification in this jurisdiction. The article then suggests an alternative for fostering critical advocacy skills in preservice programming: an Issues Analysis Project, in which teachers identify a “gap” in the field (i.e., pedagogical, ideological) and design a blueprint for action (e.g., advocacy letter, policy statement, workshop, curricular innovation) that potentially offers a resolution. The conclusions take up the broader implications of the study for language teacher identity negotiation as well as the TESOL organization's efforts in promoting advocacy amongst its membership.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.012 |
| 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".