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Knowledge Transfer Strategy

2008· book-chapter· en· W2499121740 on OpenAlexaff
Robert Parent, Denis St‐Jacques, Julie Bélievau

Bibliographic record

VenueIGI Global eBooks · 2008
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPositivismKnowledge managementTacit knowledgeKnowledge transferParadigm shiftProduct (mathematics)Computer scienceDual (grammatical number)Object (grammar)EpistemologyCognitive scienceSociologyPsychologyArtificial intelligenceMathematicsPhilosophy

Abstract

fetched live from OpenAlex

This chapter reviews recent literature on knowledge and knowledge transfer (KT) and proposes the emergence of a classification system of the core KT concepts, models, and contexts that helps address issues of a strategic nature. The two paradigms that inform most of the KT literature, the positivist and social construction paradigms, and their implications on strategy formulation, are discussed. The positivist paradigm views knowledge as an object that can be passed on mechanistically from the creator to a translator who then adapts and transmits it to the user. The social construction paradigm views knowledge as the dynamic by-product of interactions between human actors who are trying to understand, name, and act on reality. In keeping with this dual paradigm logic, the literature on KT can be categorized as originating either from an information technology paradigm or an organic paradigm. The chapter discusses how most of the past strategy-related KT issues focused on the transfer of explicit knowledge and indicates that the future direction implies a shift in attention towards more tacit knowledge transfer considerations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.868
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.057
GPT teacher head0.301
Teacher spread0.244 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations2
Published2008
Admission routes1
Has abstractyes

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