International Education Policy in Ontario: A Swinging Pendulum?
Bibliographic record
Abstract
Despite frequent references to developing an international education (IE) strategy, unlike many any other Canadian provinces, Ontario does not have an IE strategy for its post-secondary education (PSE) sector as of April 2018. However, the absence of an official IE strategy has not deterred the province from engaging in IE activities. Ontario boasts among the highest enrolments of international students in the country (43.2 percent of the approximately 124,000 international students in Canada for the year 2014 [Canada. CIC 2015]), has been engaged in multiple international partnerships at the governmental and institutional levels (Sá and Sabzalieva 2016; Trilokekar, Safdar, et al. 2014), and has developed economic and immigration policies that aim at facilitating the attraction and retention of international students (Trilokekar and El Masri 2016a; Trilokekar et al. 2014). Thus, the Ontario story raises questions about what is meant by policy: Is it essentially a published government document (i.e., a text)? Does the absence of a formal PSE government policy/strategy document suggest the absence of an IE policy in Ontario’s PSE sector? This chapter is about the Ontario story.\nDrawing on two sets of data derived from twelve semi-structured interviews with federal and Ontario government politicians (premiers and leaders of opposition parties) and bureaucrats from different ministries involved directly in IE policy-making, this chapter examines where and how IE has taken shape in the province’s PSE sector. Our primary focus is to understand how IE as policy has emerged and evolved in Ontario, that is, the decision-making aspects of the policy-making process, its context, structure, agendas, and actors. Whereas the Ontario PSE sector consists of universities, colleges, and private career colleges, this chapter is primarily focused on the provincial government and its relations with the university sector in developing an IE policy.\nThe chapter provides a brief historical narrative and identifies six key themes that speak to the unique characteristics of the Ontario’s IE policy, namely: 1) people, power, and position; 2) economy and global competition; 3) shifting federal-provincial relations; 4) parochialism: a recurring undercurrent; 5) universities in the lead; and 6) managing risks.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.032 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".