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Record W2047655912 · doi:10.1108/00251740910938948

Implementing change in public sector organizations

2009· article· en· W2047655912 on OpenAlexaff
John Cunningham, James S. Kempling

Bibliographic record

VenueManagement Decision · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsOriginalityPublic sectorValue (mathematics)Interpretation (philosophy)Public relationsFocus (optics)Management scienceFocus groupSociologyMarketingPolitical scienceBusinessQualitative researchComputer scienceEconomicsSocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to review the importance of various change principles in assisting change in three public sector organizations. Design/methodology/approach The researchers carried out interviews and used focus groups in assessing the principles and strategies which would be more useful. Findings The interview and focus group results in three public sector organizations suggest that forming a guiding coalition might be one of the most important principles to observe. Research limitations/implications The research data used for illustration are based on case evidence and the anecdotal interpretation of change in three settings. The paper does not claim to offer a scientific conclusion. Practical implications The goal is to encourage a discussion on whether or not certain principles or strategies should be more important. Originality/value The paper reviews the literature on change and reviews these principles in real experiences. Much of the other literature is conceptual.

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)0.037
metaresearch head score (Gemma)0.056
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 score0.037
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0100.012
Scholarly communication0.0090.005
Open science0.0020.007
Research integrity0.0030.004
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 head0.122
GPT teacher head0.438
Teacher spread0.316 · 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

Citations88
Published2009
Admission routes1
Has abstractyes

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