Critical pedagogies in the neoliberal university: What happens when they go digital?
Bibliographic record
Abstract
Universities everywhere have been transformed as a result of expectations of labour flexibility and the implementation of market‐oriented policies and practices including cost containment measures and efficiency‐driven management models. Online teaching can be seen as a reform to education constituted by a “common sense” vision for post‐secondary education survival. Critical pedagogical approaches in social work education, geared towards fostering critical thinking skills and an insistence on students taking into account the social and historical positioning and identities of professionals and service recipients, must function within this context. This article explores how digitalized learning spaces in online social work education programs shape critical pedagogies and create different knowledge, power, and spatialized relations as universities are being restructured along neoliberal lines. Our central question is: What gets produced in the neoliberal university when critical pedagogies go online? We situate our discussion at the intersection of four areas of literature: critical pedagogies; geographies of education; neoliberalism; and distance education. Online education is analyzed as a technology of governance that effectively restructures the workplaces of university educators while constituting and disciplining willing subjects who can respond successfully to the demands of the neoliberal university.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.060 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".