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Record W2262787732 · doi:10.26522/ssj.v9i1.1148

Fast Times in Hallowed Halls: Making Time for Activism in a Culture of Speed

2015· article· en· W2262787732 on OpenAlexaffvenueabout
Kamilla Petrick

Bibliographic record

VenueStudies in Social Justice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsYork University
Fundersnot available
KeywordsTemporalityReflexivitySociologyPoliticsTemporalitiesSocial movementArgument (complex analysis)Action (physics)Diversity (politics)Social scienceEpistemologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This article examines the implications of the social acceleration of time for the capacity of activist-scholars to engage in collective action. Drawing on interdisciplinary literature on time and temporality, the article argues that the neoliberal university is driven by the same speed imperative that underpins the capitalist mode of production, and that the resulting and growing time pressures inhibit academics' (and others') involvement in social movements in profound and deleterious ways. To explore this argument empirically, I draw on insights gleaned from semi-structured interviews with Canadian activist-scholars. Despite the manifest diversity of temporal experiences and challenges faced by scholar-activists in contemporary high-speed society, it is clear that academics today face severe time pressures that apply across individual differences and across disciplines. These pressures, which can only be properly explicated with reference to the ruling political-economic paradigm, militate against the capacity to engage in reflexive thought (for both scholarly and activist purposes) and also against a higher level of involvement of ‘public intellectuals’ in social movements. The article's conclusion offers a few tentative thoughts about tempering the speed imperative for the purpose of self-care and by extension, for the common good in the long run.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.109
GPT teacher head0.428
Teacher spread0.319 · 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 designQualitative
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

Citations9
Published2015
Admission routes3
Has abstractyes

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