Hiring Criteria and Employability of ESL/EFL Instructors in the TESOL Job Market in Canada and the United Arab Emirates
Bibliographic record
Abstract
This study investigated the hiring criteria and employability of ESL/EFL instructors in Canada and the United Arab Emirates (UAE) in higher education. It also explored challenges facing instructors and program administrators in today’s global TESOL job market, and how they both tackled these challenges. The study drew on intersectionality and Critical Race Theory and a qualitative methodological approach to answer the research questions. The research methods included an online questionnaire, analyses of online job advertisements and instructors’ journal reflections, and interviews with instructors and program administrators.\nThe findings indicate that educational qualifications, and teaching experience and certification constituted the primary hiring criteria in both countries. Also, changes in the job markets were identified including changes related to Covid-19, hiring dynamics, the use of the term “native-speaker”, and degree inflation. In addition, instructors underscored challenges they faced in the job market (i.e., limited job opportunities, job precarity, and instances of discrimination), and how they countered them by highlighting their “non-native” status and being agentive and critical of discriminatory practices. Program administrators’ voices crystalized in supporting instructors by being proactive and critical of these discriminatory acts.\nThis study underscored instructors’ and program administrators’ voices in the TESOL field by offering them the opportunity to tell their stories. The findings suggest instructors and program administrators hold comparable views, which can lay the foundation for future discussions of professional certification, and equivalency requirements, and the value of international experience and education. These findings highlight several research gaps that merit future exploration.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".