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Record W2014325388 · doi:10.1108/01437730410521822

Leading the strategic development of intellectual capital

2004· article· en· W2014325388 on OpenAlexaffabout
Irene M. Herremans, Robert G. Isaac

Bibliographic record

VenueLeadership & Organization Development Journal · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIntellectual capitalBusinessProcess (computing)Private sectorWork (physics)Plan (archaeology)Strategic planningCore competencyCompetitive advantageResource (disambiguation)Capital (architecture)Public relationsKnowledge managementManagementMarketingEconomicsComputer scienceEngineeringFinancePolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

The Intellectual Capital Realization Process (ICRP), developed by the authors, permits the leaders of an organization to develop strategies to realize the potential of intellectual capital (IC). This process is consistent with the resource‐based view of the firm, which suggests looking inward to develop core competencies for building competitive advantages. By utilizing a public sector organization as an example, this paper seeks to inform the reader of the preliminary work and subsequent steps to follow when implementing the ICRP. The Canadian Sport Centre Calgary (CSCC) organization serves this purpose, although the ICRP has also proven equally successful when used in a private sector company. The ICRP helped the CSCC identify and plan the development of its unique capabilities, relationships, and processes that benefit the organization through the creation of leadership ability and the generation of wealth.

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.015
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.008
Scholarly communication0.0150.005
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.067
GPT teacher head0.225
Teacher spread0.159 · 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 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

Citations17
Published2004
Admission routes2
Has abstractyes

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