Bibliographic record
Abstract
This article represents a call to understand school safety audits as central to processes of institutional branding. It argues that reading safety audits through a branding optic helps to draw out their uses in providing support for the augmentation of techno-security apparatuses on campuses and to contextualize them vis-à-vis increasing tendencies to govern universities in accordance with business models of management. While safety audits are generally endorsed as necessary for helping university administrators ensure the safety of students, faculty and staff, the more critical reading provided here draws attention to their entanglement with administrative efforts to construct commoditized university narratives. This paper substantiates and extends research by scholars who make note of the ongoing configurations of educational institutions in accordance with intertwining military and corporate logics. The discussion begins with a review of research by scholars who are highly critical of this trend. Next, the paper offers an exploratory case analysis of links between documents produced by one Canadian university’s administration regarding a sexual assault on that campus in 2007, the undertaking of a universitywide safety audit, and institutional investments in increased security measures. The article concludes with reflections on the importance of counter-rationalizations to this relatively new model of university governance.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".