Intersectional lens to the study of racism in <scp>TESOL</scp> leadership: A narrative inquiry of a Nonnative English‐speaking leader (<scp>NNESL</scp>) exposing epistemological and institutional racism
Bibliographic record
Abstract
Abstract Racism in TESOL and other academic fields is nothing new, nor are discussions on the topic. However, a majority of the racist encounters discussed in existing literature report on the negative experiences of language teachers and/or students. An area that has historically been ignored and is long due exploration is the negative experiences of nonnative English‐speaking leaders (NNESLs), especially when they lead and/or interact with colleagues among whom ideologies of Whiteness and native English speakerism are dominant. With an aim to fill this gap, this article provides a narrative inquiry of an NNESL's experiences of facing epistemological and institutional racism as she leads a division within an International Branch Campus (IBC) of a U.S. university in an English as an international language (EIL) context in the Middle East. As the NNESL attempts to introduce necessary innovations and policy changes, her capacity as a change maker is questioned, partly due to her nationality, nonnativeness, race, and gender. This article is an attempt to uncover the racial discrimination experienced by NNESLs by providing examples of epistemological and institutional racism embedded in racist discourses and practices, and how it, directly or indirectly, plays a significant role in power relations, institutional structures, and identities, and has implications for the field of TESOL leadership.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.041 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| 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".