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Stealing TimeThe Temporal Regulation of Labor in Neoliberal and Post-Fordist Work Regime

2007· article· en· W2132874790 on OpenAlexaboutno aff
Andrew Stevens, David O. Lavin

Bibliographic record

VenueScholarworks (University of Massachusetts Amherst) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsFordismCommodificationNeoliberalism (international relations)Resistance (ecology)IdeologyCapital (architecture)Political economyPoliticsIndustrial relationsSociologyMarket economyPolitical scienceEconomicsManagementLaw

Abstract

fetched live from OpenAlex

The construction, commodification, and regulation of in workplaces should be seen as a political economic process. This article examines the emergence of time as it arose in a neoliberal ideological climate. The rise of theft as a new type of crime against capital is contextualized within organizational changes that demarcate neoliberal economies, namely the emphasis on decentralized, post-Fordist, outsourced, and increasingly competitive labor-market conditions. This paper begins by examining the theoretical elements of theft and moves on to draw from empirical evidence gathered from a case study of an outsourced, non-unionized call center in Ontario, Canada, and explores how workers' is regulated by management using advanced information and communication technologies. It concludes that despite the gains made by individualized acts of resistance and informal collective resistance by employees in call centers which make the working environment somewhat tolerable, collective resistance through unionization is central to make far-reaching changes in the sector.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.042
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2007
Admission routes1
Has abstractyes

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