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Record W2024777897 · doi:10.1108/14691930210448314

Organizational memory and intellectual capital

2002· article· en· W2024777897 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

VenueJournal of Intellectual Capital · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsIntellectual capitalOrganizational memoryMnemonicRelational capitalPerspective (graphical)NarrativeIndividual capitalKnowledge managementHuman capitalComputer scienceOrganizational learningEconomicsFinancial capitalPsychologyCognitive psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Organizational memory (OM) is a branch of collective memory studies tied to instrumental action which seeks to enhance the organization’s intellectual capital by aiding organizations in using both routine practices and imbedded information to anticipate and solve problems. Within an intellectual capital perspective, OM involves the encoding of information via suitable representation and retrieval systems which are filtered through the three forms of intellectual capital – human, structural and relational. This paper explores how these three forms of intellectual capital, when put into mnemonic practice, generate four interrelated but distinct models of OM – the storage bin model, the narrative model, the innovative model, and the political resource model. Emphasis is placed on discussion of how each of these models of OM impacts efforts to effectively manage an organization’s intellectual capital.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.019
GPT teacher head0.196
Teacher spread0.177 · 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