Bibliographic record
Abstract
There has been increasing recognition of the need to pursue critical research in the fields of ESL/EFL; however, the role that race plays in our research practices has not been frequently discussed. In-depth explorations of how a racialized identity shapes (and is shaped within) complex interactions between the researcher and researched can uncover the ways that race affects all aspects of our investigations, from collecting data to reporting. This article presents personal narratives of two ESL/EFL researchers, White and Asian, who critically reflect on the implications of racialized identities in conducting their respective studies. Both authors' accounts share a common theme of tensions around researcher positionality, locatability, (self-)reflexivity, and how best to represent those we are researching and writing about. However, while the first author brings to the fore the complexities of race and racism in ESL/EFL research through her narrative of studying “the other,” the second author attempts to further complexify these issues by highlighting the distinctly unique tensions which arise when a researcher of color attempts to study “her own kind.” The report will thus contribute to an enhanced understanding of the intersections of postcolonial identities, race, and critical research methodologies and ideologies in the TESOL field.
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 | 0.059 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.026 | 0.054 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".