Post-Student Cap Analysis in Ontario, Canada: Faculty's Perspectives a Vital Component in Understanding the Impact of International Student Caps
Bibliographic record
Abstract
This survey study, uniquely employing Jahoda's Latent Deprivation Model, investigated faculty attitudes and challenges due to the federal government's decision regarding international student caps in Public-Private Partnerships (PPPs). The study used a structured questionnaire to investigate faculty perceptions of financial, professional, and personal impacts. The study aimed to capture faculty perceptions of international caps and the potential for job losses in higher education. The survey was restricted to faculty members affiliated with higher education institutions, particularly PPPs. Based on statistical correlations and data analysis from 165 participants, the author provided comprehensive findings and recommendations for understanding the issue from faculty members' viewpoints. The author suggested ways to support faculty and ensure academic excellence in higher education as part of the recommendations. In terms of demographics, 96 percent of the respondents were minorities, and 78.4 percent strongly agreed that the decision adversely impacted their financial and emotional well-being. Data analysis revealed a strong correlation between psychological health and faculty career impact. The study recommended revisiting the situation in a fresh light and considering other stakeholders who are often overlooked to avoid future economic and social hardships. According to the author, faculty in higher education should receive support and guidance as well as incentives and compensation, such as a waiver of the Ontario Teaching Certificate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".