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Record W3087328426 · doi:10.1177/0170840620964072

Office Design, Neoliberal Governmentality and Professional Service Firms

2020· article· en· W3087328426 on OpenAlexafffund
Claire-France Picard, Sylvain Durocher, Yves Gendron

Bibliographic record

VenueOrganization Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of OttawaUniversité Laval
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGovernmentalityMarketizationSociologyService (business)Public relationsNeoliberalism (international relations)Government (linguistics)BusinessMarketingPolitical sciencePolitical economyPoliticsLaw

Abstract

fetched live from OpenAlex

This paper examines how neoliberal governmentality is conveyed and promoted through office design technologies within professional service firms (PSFs). Our data, constituted through interviews with firm representatives and site visits, points to the pursuit by PSF management of a core principle of marketization, which is promoted through a range of spatial technologies inscribed in the office space to sustain the development of subjectivities reflective of Homo economicus. Specifically, we found that fluid open-plan layouts and adaptable workplaces constitute technologies of government with great ambitions, aiming to cultivate a paradoxical climate of cooperative competitiveness within the firm, a constant endeavor toward efficiency, and a transformation of firm members into neoliberal self-entrepreneurs. One of the chief ideas that motivates office designers is to provide PSF members with the freedom to work how, where, and when they want in order to meet their firm’s imperatives of effectiveness and client satisfaction. Ultimately, our study shows that office design initiatives within PSFs constitute tools of neoliberal governmentality that aim to govern subtly the emancipation of firm members as accomplished self-entrepreneurs.

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 score0.278
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.044
GPT teacher head0.243
Teacher spread0.198 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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