The Journey to Becoming an English Academic Writing Instructor as a Non-Native English-Speaking Teacher
Bibliographic record
Abstract
This presentation is an autoethnography that explores my identity as a non-native English-speaking writing instructor. The presentation narrates my feelings, opinions and observations about my educational journey from when I was an undergraduate student in the English department and first introduced to academic writing in English, to becoming a non-native English-speaking teacher (NNEST), until I became an English academic writing instructor. Throughout my years of teaching English to non-native speakers and teaching English academic writing, being foreign to the English language has always had a presence either in the way I identified myself or in the way my society labeled me. Society focused mainly on my non-nativeness to English. However, I was able to self-consciously see my strength of understanding and comparing the writing norms of my first culture with those of the target culture, which was reflected on my teaching in the classroom. Much of the research on NNESTs has focused on issues such as their pronunciation, vocabulary, students’ perceptions, but this study focuses on how I negotiated my identity in teaching writing.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".