Slowing things down: taming time in the neoliberal university using social work distance education
Bibliographic record
Abstract
The neoliberal university is described as a space where there is an ever-present ‘scarcity of time’ as faculty face increasingly high-paced demands for efficiencies and productivity. By this logic, students are produced as self-enterprising individuals, steeped in the values of competition, and solely invested in enhancing their human capital. Within this context, online education has gained prominence as an alternative to on campus, face-to-face post-secondary education. In this article, we draw on findings from qualitative interviews conducted with social work educators who teach using online-based pedagogy as well as recent graduates who completed their social work education in distance learning programmes. Our research explores how distance education shapes the pace of knowledge production in Canadian Schools of Social Work where a mandate to promote social justice-based professional practices coincides with time constraints associated with neoliberalism. Building on conceptualizations of temporality, we found that when mobilized as a time-saving measure, online programmes can exacerbate the intensified workload for both teachers and students, and they can also limit potential for equity and inclusion in the university. However, when mobilized as a ‘time-taming’ measure, adequately resourced distance social work education programmes offer possibilities of resistance to pressures faced in post-secondary institutions.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".