The Costs of Inclusion: Debt, Migration, and the Privatization of Post-Secondary Education in Canada
Bibliographic record
Abstract
This dissertation explores how the benefits of post-secondary education are affected by constrained public spending, wherein a growing burden of associated costs are transferred onto students.\n\nIn pursuit of this investigation, I engage a feminist political economy approach to develop and apply the notion “predatory inclusion”—or the racialized, gendered, and classed processes through which the extension of opportunities for socio-economic advancement to formerly excluded social groups undermine the benefits of access and reinforce privileges of more powerful actors. Informed by this approach, which highlights the expropriative character of the financialization and internationalization of social reproduction, my central contention is that the normalization of student debt and the growth of educational migration—each cast by governments and institutions as key to expanding student access—foster predatory inclusion. \n\nThis dissertation unfolds in 5 substantive parts. Chapter 1 provides an overview of my theoretical and methodological approach to comparing outcomes among domestic and international students. Next, Chapter 2 sketches the roots of post-secondary education’s role in addressing private-sector interests and constructing criteria for national belonging within the settler colonial capitalist Canadian state. Chapter 3 then evaluates the distinct approaches to privatization, adopted by four Ontario-based post-secondary institutions, that reallocate a disproportionate burden of the costs of education onto students. Against this backdrop, Chapters 4 and 5 highlight how two different groups of post-secondary graduates—domestic students reliant on government-sponsored loans and international students with insecure residency status—face higher odds of filling precarious jobs that more socioeconomically secure graduates, including debt-free domestic students, do not wish to take on. \n\nChallenging the foremost assumptions of the social investment policy framework, which aims to balance neoliberal austerity measures with labour market activation strategies and demands for greater socioeconomic equality, this dissertation documents the significance of predatory inclusion in Canada’s public universities and colleges and its effects. In revealing how contemporary terms of inclusion in post-secondary education serve to reproduce social inequality on the basis of citizenship status, race, country of origin, socioeconomic class, and gender, my findings underscore the need for alternative policy directions designed to better serve low-income, migrant, and otherwise marginalized students.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".