Introduction: Identity, Transdisciplinarity, and the Good Language Teacher
Bibliographic record
Abstract
What constitutes a “good teacher” and “good teaching” has come under much scrutiny in an age of globalization, transnationalism, and increased demands for accountability. It is against this evolving landscape and the pathbreaking work of the Douglas Fir Group (DFG, 2016) that this special issue engages the following two broad questions: (a) In what ways is language teaching “identity work”? and (b) To what extent does a transdisciplinary approach to language learning and teaching offer insight into language teacher identity? We begin this Introduction with a discussion on identity research in second language acquisition and applied linguistics, and then address innovations in language teacher identity research, exploring how this work has been advanced methodologically through narratives, discourse analysis, and an ethical consideration of research practices. We then consider how the transdisciplinary framework of the DFG, and its focus on macro, meso, and micro dimensions of language learning at the ideological, institutional, and classroom levels, respectively, might contribute to our understanding of language teacher identity. In the final section, we argue that the host of complementary theories adopted by the six contributors supports the view that a transdisciplinary approach to language teacher identity is both productive and desirable. Further, the contributors advance the language teacher identity research agenda by taking into consideration (a) how teacher identity intersects with the multilingual (Higgins and Ponte) and translingual (Zheng) realities of contemporary classrooms, (b) the investment of teachers in developing the semiotic repertoires of learners (Stranger–Johannessen and Norton) and a socially inclusive learning environment (Barkhuizen), and (c) the emotions (Wolff and De Costa) and ethical practices (Miller, Morgan, and Medina) of teachers. Central to all articles in this special issue is the need to recognize the rich linguistic and personal histories that language teachers bring into the classroom in order to promote effective language learning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".