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Record W2918674493 · doi:10.1177/0018726719828439

The changing nature of managerial work: The effects of corporate restructuring on management jobs and careers

2019· article· en· W2918674493 on OpenAlexaff
William Foster, John Hassard, Jonathan Morris, Julie Wolfram Cox

Bibliographic record

VenueHuman Relations · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Downsizing and Restructuring
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRestructuringContext (archaeology)Work (physics)Argument (complex analysis)BureaucracyPublic relationsSociologyPerceptionPolitical sciencePsychologyPolitics

Abstract

fetched live from OpenAlex

This article analyses contemporary issues relevant to understanding the changing nature of management and managerial work. The argument is developed in four parts. First, to provide context, we offer an overview of the literature on the organization and control of managerial work, tracing contributions mainly from the early 1950s onwards. Second, we discuss the first of two related concerns relevant to understanding the contemporary nature of managerial work – strategies of organizational restructuring: an analysis highlighting the role of downsizing and delayering within corporate campaigns promoting ‘post-bureaucratic’ systems. Third, we extend this discussion by addressing how such corporate restructuring affects managers in their everyday work – notably in relation to the perceptions and realities of growing job insecurity and career uncertainty: an analysis that frequently draws upon our own investigations to establish an agenda for future research. The article concludes by summarizing the content of four research articles whose arguments relate to issues discussed in this analysis of managerial work.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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 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

Citations58
Published2019
Admission routes1
Has abstractyes

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