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Record W2060756892 · doi:10.1108/00251740910960123

A cluster analysis of the KM field

2009· article· en· W2060756892 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManagement Decision · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOriginalityPerspective (graphical)Dimension (graph theory)Cluster (spacecraft)SociologyField (mathematics)Value (mathematics)Knowledge managementContent analysisSocializationManagement scienceComputer scienceQualitative researchSocial scienceEngineering

Abstract

fetched live from OpenAlex

Purpose The main purpose of this study is to review the knowledge management literature from a content‐related perspective using cluster analysis. Design/methodology/approach A critical analysis of previous review articles in KM provided a conceptual framework with nine dimensions. A survey was then administered to 120 KM authors asking them to review which dimensions they considered in their own research. Findings Three clusters of KM research were identified as follows: the socialization school, the collaboration school, and the codification school. Research limitations/implications The study does not consider the dimension of strategic versus operational KM issues nor does it consider any non‐Anglophonic research. Practical implications The three identified clusters accrued from the review provide both scholars and practitioners with a more holistic perspective and better understanding of the main thrusts of their KM initiatives. Originality/value The research is the first systematic and comprehensive review of KM that provides a cluster analysis approach.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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