Commercializing Higher Learning through the Discourse of Skills in University Co-Operative Education: Tensions and Contradictions.
Bibliographic record
Abstract
The last 3 decades have witnessed the rise of reforms aimed at conjoining Canadian universities to the economic system. Critics have pointed out how reforms have emphasized economic utility in universities to the detriment of their sociocultural mission, producing negative effects. One curricular innovation that has spread in tandem with reforms is co-operative education (co-op), which is seen to improve economic utility but has not attracted critical scrutiny. This article offers a socially critical perspective on co-op that draws on conceptual work and a sub-set of empirical data from a multi-case study conducted in one university. A tentative portrait emerged of a previously unexplored avenue of commercialization that is mediated through co-op. The process began with students enrolling to deal with personal financial burdens and to feel more secure about their economic futures. Once in co-op, students were exposed to competitive market processes that immersed them in the commercial activity of packaging, exchanging, and accumulating their human capital, using “skills” as discursive currency. Students internalized the discipline of the market, taking an entrepreneurial stance towards their self-definition and presentation. Programmatic features did not to enable students to reflect on or remediate negative experiences (e.g., tacit or explicit sexism) or distorting effects (e.g., devaluation of sociocultural skills). Their experiences highlighted areas for further critical investigation: the devaluation of the liberal arts; power dynamics and asymmetries between employers, administrators, and students; and patterns of social relations (e.g., the exercise of gendered power) in labour markets, workplaces, and universities. The article concludes that a rebalancing of economic and sociocultural purposes is needed in co-op, and the scope of critical inquiries into economistic reforms should be extended to include micro-level effects produced through the market-driven processes at the heart of co-op.
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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.086 |
| Scholarly communication | 0.034 | 0.022 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.009 |
| 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".