Nihonjin Kyoushi Dake?: The Perceptions and Beliefs of a Non-Native Speaking Teacher in a High-intermediate Japanese Language Class
Bibliographic record
Abstract
Within non-native speaking teacher (NNST) research, literature concerning NNSTs within the Canadian Japanese-as-a-foreign language (JFL) context is limited. Previous research has shown that prevailing preferences for NSTs due to perceived linguistic and pedagogical capabilities creates negative implications for NNSTs, such as teaching anxiety, confidence issues, and workplace challenges (i.e., hiring and discrimination) (Holliday, 2006; Phillipson, 1992; Kickzokiak & Wu, 2018; Faez & Karas, 2017; Park, 2012; Tsuchiya, 2020). By using Scholarship of Teaching and Learning (SoTL) and qualitative and quantitative methods (i.e., reflexive journal entries, pre- and post-course surveys, language logs, follow-up interviews, and Likert-scale questions), this study addresses the gap of scarce literature on NNSTs in the Canadian JFL field by investigating the instructional practices used by a NNST and students’ and the instructor’s perceptions and beliefs of the NNSTs’ capabilities in a high-intermediate Japanese class. Key findings of this study are that tasks benefit students’ learning of professional Japanese communication, NNSTs have the capabilities to teach high-level and pragmatic-focused speaking courses, and, students’ preferences for their instructor are based on their instructors’ individual skills and teaching attitudes rather than their nativeness. These insights provide valuable implications for academic and practical fields, offering novel findings about NNST capabilities. Administrators can use this information for more informed hiring decisions and establish collaborative models based on the unique strengths of both NSTs and NNSTs. These recommendations foster hope for NNSTs by advocating for equity, diversity, and inclusion, thereby transforming student learning within higher education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".