Marketing mistakes or unethical marketing in higher education? Two case stud-ies in Ontario Colleges
Bibliographic record
Abstract
Using the lens of the commercialization of higher education, this paper scrutinizes how marketing efforts are made to recruit international students as educational consumers in Ontario, Canada. Specifically, this paper examines how the government’s immigration policy directly and explicitly encourages the public college system to take advantage of the immigration policy and use it as a selling point in educational marketing. To stay competitive in the educational market, the two case study colleges initiated various programs specifically designed to satisfy international students’ immigration needs and wants. They also implemented various marketing tools to convey this message and advertise their capabilities. While some students eventually become educational consumers of these programs, they also unexpectedly became plaintiffs filing lawsuits against their education service providers. They claimed that false marketing communications had impaired their rights; the colleges became defendants, defending their marketing mistakes as is done in the for-profit private sector. This paper concludes by providing potential guidelines to help maintain ethical marketing in the context of a commercialized higher education market.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| 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".