MétaCan
Menu
Back to cohort
Record W2987492879 · doi:10.1177/0964663919880303

Yelling ‘Fire’ in a Crowded Occupation: Cynical Fire Hazard Claims and the Technocratic Containment of Dissent

2019· article· en· W2987492879 on OpenAlexaff
Honor Brabazon

Bibliographic record

VenueSocial & Legal Studies · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDissentCONTESTContext (archaeology)PoliticsSociologyPolitical sciencePublic sphereSocial movementLawLaw and economicsPolitical economyPublic administration

Abstract

fetched live from OpenAlex

While the privatisation of public space has been the subject of considerable research, literature exploring the shifting boundaries between public and private law, and the role of those shifts in the expansion of neo-liberal social relations, has been slower to develop. This article explores the use of fire safety regulations to evict political occupations in the context of these shifts. Two examples from the UK student occupation movement and two from the US Occupy movement demonstrate how discourses and logics of both private and public law are mobilised through fire hazard claims to create the potent image of a neutral containment of dissent on technical grounds in the public interest – an image that proves difficult to contest. However, the recourse to the public interest and to expert opinion that underpins fire hazard claims is inconsistent with principles governing the limited neo-liberal political sphere, which underscores the pragmatic and continually negotiated implementation of neo-liberal ideas. The article sheds light on the complexity of the extending reach of private law, on the resilience of the public sphere and on the significance of occupations as a battleground on which struggles over neo-liberal social relations and subjectivities play out.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.341

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)0.0000.000

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 head0.023
GPT teacher head0.285
Teacher spread0.261 · 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 designObservational
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueSocial & Legal StudiesSame topicRegulation and Compliance StudiesFrench-language works237,207