The Decentering of Native English Speaking Teachers in English as a Lingua Franca Contexts
Bibliographic record
Abstract
English’s spread throughout the world has made it the most widely studied second language in the world and the de facto lingua franca. Though the purpose of a lingua franca is to allow speakers with varied native languages to have a common language to communicate with, there are historical and social factors such as colonialism and racism that have created inequities in the field between native English speaking teachers and their non-native English speaking teacher colleagues. This project aims to to present the conditions that led to inequities, address discriminatory practices in the field, and provide opportunities for newly minted native English speakers to recognize and mitigate their own privileges and position in the field. This project was influenced by the research and writings of Jenkins (2014, 2015), Canagarajah (1999a, 1999b), Motha (2014, 2020), and many others exploring the history, role, and impact of English as a lingua franca. The framework for the project was informed by guidelines created by The University of Toronto (2021). It is designed to be a self-guided professional development tool for Teaching English as a Foreign Language certificate graduates. The three sessions will be conducted online, providing facts about the history and current realities in the field, while asking participants to focus on self-reflection and how they can be agents for change creating a more equitable English as a lingua franca field.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".