When the Native Is Also a Non-native: “Retrodicting” the Complexity of Language Teacher Cognition
Bibliographic record
Abstract
Abstract: The impact of native (NS) and non-native speaker (NNS) identities on second or foreign language teachers’ cognition and practices in the classroom has mainly been investigated in ESL/EFL contexts. Using complexity theory as a framework, this case study attempts to fill the gap in the literature by presenting a foreign language teacher in the United States who teaches French as a NS and German as a NNS teacher, at the college level. Specifically, the study explores the interface between NS/NNS identities, teacher cognition, practice, and professional identity. The retrodictive qualitative analysis of semi-structured interviews and classroom observations reveals that the participant teacher’s NS French and NNS German identities influenced her teacher cognition, specifically in beliefs about teaching grammar. In addition, previous language learning experiences affect current decision-making processes in teaching. In terms of teaching practice, the dual NS French and NNS German identity affects teaching practice in the formal areas of language, target culture knowledge and awareness, teaching style, and perceptions about language varieties. Professional identity is construed here as being the mediator of target second language (L2) cultures in the classroom. The implications of teacher cognition as a complex system for L2 teaching and teacher education are discussed.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".