Bibliographic record
Abstract
The profession of teaching English to speakers of other language (TESOL) has experienced significant changes in the past decades, and career development in the contemporary era is becoming increasingly complex and unpredictable. This study aimed at finding the patterns and attractors that contribute to successful careers in TESOL from the perspectives of graduates. Data were collected through a comprehensive survey of international and Canadian TESOL certificate graduates at a mid-size university in British Columbia and through interviews with several of the graduates. The results were analysed through the lens of the forms of capital (Bourdieu, 1986) and chaos (Bright & Pryor, 2005) theories. The results indicated a significant diversity of TESOL employment and the varied effectiveness of factors in career development amongst participants. Graduates experienced challenges in terms of their TESOL skills and their job search skills, with both being impacted by the forms of capital they held in the TESOL profession. While TESOL students and early career TESOL professionals need to be more prepared for the complexity and unpredictability of TESOL careers by continuously improving their forms of capital, TESOL teacher educators, TESOL program administrators, and TESOL professional organizations should take the diverse needs of students with different backgrounds into consideration and provide long-term career support to build a robust TESOL community.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".