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Record W4390063487 · doi:10.1093/jpo/joad025

Organizational dominance and the rise of corporate professionalism: The case of management consultancy in the UK

2023· article· en· W4390063487 on OpenAlexaff
Ian Kirkpatrick, Daniel Muzio, Matthias Kipping, Bob Hinings

Bibliographic record

VenueJournal of Professions and Organization · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of AlbertaYork University
Fundersnot available
KeywordsDominance (genetics)Public relationsMacroProcess (computing)Political scienceField (mathematics)Macro levelBusinessManagementSociologyAccountingEconomicsEconomic system

Abstract

fetched live from OpenAlex

Abstract While recent debates about the professions have noted the pervasive influence of organizations, less is known about how this plays out at the macro or occupational level. In this paper, we address this concern, focusing on corporate professionalism (CP) as an emergent form which appears to be shaped by organizational interests. Drawing on relational perspectives of professions, we focus on the strategies of two associations in the UK management consultancy field over a 50-year period. Our analysis of archival and interview data reveals how, over time, both associations substantially modified their strategies in response to shifting priorities of firms employing large numbers of consultants—abandoning early commitments to occupational professionalism in favor of a corporate form. A key contribution of the paper is to develop a process model for understanding how and why CP emerges. We also highlight the need to pay more attention to the often neglected role of employing organizations in accounts of professional formation in contemporary society.

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)0.007
metaresearch head score (Gemma)0.026
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.335

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0130.014
Scholarly communication0.0080.003
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.250
Teacher spread0.228 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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