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Record W1983488524 · doi:10.5430/ijba.v3n4p37

Theorising the Concept of Organisational Artefacts: How It Enhances the Development of Corporate/Organisational Identity

2012· article· en· W1983488524 on OpenAlexvenueno aff
Olusoji James George, Oluwakemi Owoyemi, Uchechi Onakala

Bibliographic record

VenueInternational Journal of Business Administration · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Identity and Reputation
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Logos Bible SoftwareCorporate identitySociologyOrganizational cultureCorporate communicationOrganisational changeBusinessPublic relationsKnowledge managementMarketingStakeholderPolitical scienceComputer scienceAesthetics

Abstract

fetched live from OpenAlex

Organisational artefacts are materials, buildings, symbols, names, images, logos, catchwords that make sense to all the stakeholders of an organisation; they therefore have meanings and do not just exist. Artefacts demonstrate the culture, norms and values of those who are in the organisation as well as all its stakeholders. In a nutshell it is a medium of communication within the members of the organisation and those outside the organisation; it makes enormous statements. This paper employing the descriptive/historical approach will examine the impacts of organisational artefacts on business organisations including how it helps in the development of a corporate identity in the organisations. This is more so as an organisation’s identity –expressed partly through organisational artefacts- goes directly to the heart of the existence of the organisation. The study concludes that directly and indirectly, knowingly and unknowingly, organisational artefacts enhance the development of corporate identity.

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)0.010
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0050.046
Scholarly communication0.0150.018
Open science0.0020.008
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.046
GPT teacher head0.273
Teacher spread0.226 · 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 designTheoretical or conceptual
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

Citations9
Published2012
Admission routes1
Has abstractyes

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Same venueInternational Journal of Business AdministrationSame topicCorporate Identity and ReputationFrench-language works237,207