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Record W1494502197 · doi:10.3138/topia.28.65

University Branding Via Securitization

2012· article· en· W1494502197 on OpenAlexvenueaboutno aff
Julie Gregory

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2012
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsAuditConstruct (python library)Corporate governancePublic relationsReading (process)SociologySecuritizationPolitical scienceBusinessManagementAccountingEconomicsComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.112
GPT teacher head0.400
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2012
Admission routes2
Has abstractyes

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