Knowledge mobilisation in education in Canada and the role of universities
Bibliographic record
Abstract
Introduction This chapter is organised around different dimensions related to research mobilisation in Canada, with a focus on the field of education and the role of universities. Major features of Canada as a country are described first to serve as a background to the chapter. The next section introduces the role of government, including current research funding agencies in Canada and issues of research quality indicators and research capacity building. Next, the chapter focuses on the strategies and mechanisms currently used by universities to share their research. The chapter concludes with a discussion about the key debates and considerations around education research mobilisation and identifies some unresolved issues that might help guide future research. Overview of Canada Canada is the world's second largest country in area. Canada is a large, rich, geographically and demographically diverse country with an advanced industrial and service economy. The term ‘cultural mosaic’ is commonly used to describe the multicultural nature of Canadian society. Canada has one of the highest immigration rates in the world (more than 1% per year). Nearly 20% of Canadians were born outside the country, a proportion that is increasing steadily, and these immigrants come from all parts of world (www12.statcan.ca/censusrecensement/ 2006/as-sa/97-557/p2-eng.cfm). People who come from different origins and cultural groups are able to retain their religions and customs as well as languages. Canada also has a high-achieving education system, consistently among the highest-ranking countries on international assessments such as PISA (Programme for International Student Assessment) or PIRLS (Progress in International Reading Literacy Study). Mandatory school age for young people is from 5–7 to 16–18 years old depending on the province. The adult literacy rate is 99%, although Canada does not score as highly on international assessments of adult literacy as it does for school-age skills (Conference Board of Canada, www.conferenceboard. ca/hcp/details/education.aspx). English and French are both official languages at the federal level. Canada has a federal political system. Power is divided between the federal government and provincial or territorial governments. Canada's provinces range in size from Ontario, with 13 million people, to Prince Edward Island, with about 200,000 people; the territories have even smaller populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".