Bibliographic record
Abstract
Although the global workforce becomes increasingly diverse, many minority groups are still standing in the path of multiple forms of exclusion. Among them are the non-White and non-native English-speaking teachers who are striving to prove their credentials and secure their careers throughout the world. The purpose of this paper is to examine the challenges faced by two Vietnamese ESL teachers pursuing their careers in Ontario, Canada. The researchers utilized a collaborative autoethnography approach developed by Ngunjiri et al. (2010) to share and analyze their experiences. This involved four key steps: preliminary data collection, subsequent data collection, data analysis and interpretation, and report writing. Through this iterative process, they engaged in both individual and team activities, revisiting previous steps to enhance data collection, analysis, or interpretation as needed. The findings revealed the unique obstacles that they encountered from various sources, including society, schools, students, and native-speaking colleagues. These challenges encompassed systemic discrimination against minority Asian professionals when recrediting their credentials, marginalizing the hiring process and being treated as outsiders within the field. By amplifying their unheard voices, the researchers aim to contribute to a more inclusive and equitable English as a Second Language (ESL) industry in Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".