Perceptions of ESL Program Management in Canadian Higher Education: A Qualitative Case
Bibliographic record
Abstract
ESL programs at post-secondary institutions must often generate revenue in addition to teaching students English. Institutions often impose explicit expectations on these programs to generate profit, creating unique challenges for those who administer them. This qualitative case study investigated challenges faced by ESL program directors at one university in Canada. Semistructured interviews were used to collect data from program directors ( N = 3) on topics relating to administration, marketing, the mandate to generate revenue, and the complexities of ESL program legitimacy and marginalization in higher education contexts. Five key themes emerged from the data: (a) the necessity for directors to be highly qualified and multilingual, as well as have international experience; (b) a general lack of training, support, and resources for program directors; (c) institutional barriers such as working with marketers and recruiters with little knowledge of ESL contexts; (d) program fragmentation and marginalization on campus; and (e) reluctance to share information and program protectionism. Findings point to the need for increased training and support for ESL program directors, along with the need for institutions to elevate the profile of these programs so they are not viewed as having less value than other academic programs on campus. https://doi.org/10.26803/ijlter.16.9.2
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".