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Record W2395995070

Practical Relevance of Knowledge Management and Intellectual Capital Scholarly Research: Books as Knowledge Translation Agents.

2010· article· en· W2395995070 on OpenAlexaff
Alexander Serenko, Nick Bontis, Emily Hull

Bibliographic record

VenueJournal of the Association for Information Systems · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsMcMaster UniversityLakehead University
Fundersnot available
KeywordsRelevance (law)ModerationIntellectual capitalScholarly communicationSociologyComputer scienceLibrary scienceKnowledge managementPsychologyPublic relationsPublishingPolitical scienceLiteratureArt
DOInot available

Abstract

fetched live from OpenAlex

To enhance our understanding of the relevance of knowledge management/intellectual capital (KM/IC) academic research, this study explores what sources authors utilize to develop their book content. Ten prominent KM/IC book authors were interviewed to identify if and how KM/IC academic literature is being disseminated through books. It was found that the body of knowledge existing in peer-reviewed journals is utilized in the development of book/textbook content. Books serve as knowledge translation agents through which academic literature is summarized, aggregated and transformed into the format that may be easily comprehended by non-academics. In addition to peer-reviewed journals, KM/IC book authors utilize other sources, including personal research, experts’ opinions, experience, practitioner magazines, conferences, books, and informal discussions with academics. The model, which was developed within this study, demonstrates that the book’s target audience and author’s motivation serve as a pure moderator of the relationship between the available content sources and actual book content.

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 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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.006
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.088
GPT teacher head0.331
Teacher spread0.243 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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