Gigification of English Language Instructor Work in Higher Education: Precarious Employment and Magic Time
Bibliographic record
Abstract
Abstract This article describes how discourses of professionalism, insecurity, and exploitation among English as a second language/English for Academic Purposes (hereinafter ESL/EAP) instructors and curriculum‐level administrators at two Canadian universities relate to their understanding of fair work. These understandings are examined in a nested manner, in keeping with social positioning theory. Via discourse and thematic analysis of job advertisements and semi‐structured interviews, we illuminate aspects of the gigification of ESL/EAP in Canada, wherein ESL/EAP instructor work is increasingly rendered un(der)paid, constantly evaluated, surveilled, and precarious. Viewed through the lens of “magic time,” an infinite category of work time, we document the frustrations of ESL/EAP instructors who recognize their own exploitation. The relevance of this study is described in relation to the growing numbers of international students at English‐speaking universities throughout the world requiring a robust program infrastructure supporting their success, while the ESL/EAP instructors who provide these programs are increasingly made disposable through contingent employment relationships. The increasing reliance on contract professors teaching for‐credit courses in higher education has come to be known as adjunctification. In the noncredit, the more marginal context of ESL/EAP instructors subject to the forces of international student supply and demand, underpaid even by contract faculty standards, and engaged in often cutthroat competition for the few remaining contracts, we reference contextual differences by calling it gigification.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.039 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".