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Record W3185077581 · doi:10.1017/9781108634724.007

Swedish Government Agencies’ Hiring of Policy Consultants: A Phenomenon of Increased Magnitude and Importance?

2019· book-chapter· en· W3185077581 on OpenAlexaff
Caspar van den Berg, Michael Howlett, Andrea Migone, Michael Howard, Frida Pemer, Helen Gunter

Bibliographic record

VenueCambridge University Press eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsInstitute of Public Administration of CanadaSimon Fraser University
Fundersnot available
KeywordsPublic sectorLegitimacyExtant taxonGovernment (linguistics)PhenomenonPublic relationsPublic administrationBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

In many Western countries, the public sector has been one of the fastest growing sectors in the consulting markets (Glassman and Winograd 2005; Saint-Martin 2012). Apart from consulting services related to ICT, public organizations hire consulting services to the extent that the public sector now forms the third-largest client sector for management consulting services in Europe (FEACO 2010, 2016). Extant research has tended to explain organizations’ hiring of consultants with either rational arguments related to organizations’ need for expertise and resources (Armbrüster 2006; Canbäck 1998, 1999), or the individual managers’ need for reducing uncertainty and gaining legitimacy (Alvesson and Johansson 2002; Berglund and Werr 2002; Clark and Salaman 1996; Fincham 2012).

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)0.006
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.012
Science and technology studies0.0060.006
Scholarly communication0.0120.004
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.003

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 head0.036
GPT teacher head0.274
Teacher spread0.238 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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