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Record W1495587050 · doi:10.1108/09696470510626766

Knowledge sharing and strategic capital

2005· article· en· W1495587050 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

VenueThe Learning Organization · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsLeverage (statistics)Knowledge managementKnowledge sharingOriginalityMacroValue (mathematics)Field (mathematics)Strategic managementBusinessMacro levelComputer scienceMarketingPsychologyArtificial intelligenceEconomicsSocial psychologyEconomic system

Abstract

fetched live from OpenAlex

Purpose This article seeks to propose that the success of an organization's knowledge‐sharing strategy and the magnitude of its strategic capital are critically dependent on its having the capability to visualize relationship‐networks among its employees, and means to identify and leverage, as appropriate, patterns of positive or negative influence. Design/methodology/approach The paper is based on the author's own experiences and those of other authors in the same field. Findings There seems no evidence in the literature that programs can be mounted to deliberately develop opinion leaders by helping them acquire such meta‐capabilities or assume archetypical characteristics. Originality/value Utilization of the NVA‐based approach described here will provide an enhanced real‐world understanding of how the various sectors and network layers of an organization coalesce, and relate to one another, at micro and macro levels.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.707

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 science0.0000.000
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 head0.031
GPT teacher head0.289
Teacher spread0.258 · 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