Markers of entanglement: Survival strategies within the neoliberal university and the promise of carceral futures
Bibliographic record
Abstract
Within the neoliberal era, the university's form and function have shifted. These shifts necessitate an unraveling of the synergies of institutions of higher education with carceral institutions. Building from the scholarship of the “college-prison nexus” and the “academic-prison symbiosis,” this paper converges on the criminology department's role within these synergies. Based on an analysis of department websites and the introductory course syllabi of English-speaking criminology departments in Canada (n = 50), I interrogate the methods used to advertise to students. I identify six markers of entanglement that are part of how departments market themselves in the neoliberal era to the student–consumer. These markers include career prospects, field placements, faculty research, pracademics, job training, and dual/bridging degrees. Utilizing these markers as a departure point, I analyze these indicators of relationships that exist between the university and the carceral apparatus. In doing so, I interrogate how these relationships can (re)produce carceral logics and systems and offer the university an articulated pathway of survival through carceral intrenchment.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".