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Record W2018786557 · doi:10.4018/joeuc.2008010102

Knowledge Appraisal and Knowledge Management Systems

2008· article· en· W2018786557 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 Organizational and End User Computing · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsKnowledge managementIntellectual capitalPersonal knowledge managementKnowledge value chainKey (lock)Critical appraisalProcess (computing)Computer scienceKnowledge engineeringBusinessProcess managementOrganizational learning

Abstract

fetched live from OpenAlex

Knowledge management (KM) is a critical practice by which a firm’s intellectual capital is created, stored and shared. This has lead to a rich research agenda within which knowledge management systems (KMS) have been a key focus. Our research reveals that an important element of KM practice—knowledge appraisal— is considered in only a fragmentary and incomplete way in research. Knowledge appraisal reflects the multi-level process by which a firm’s knowledge is evaluated by the organization or individual for its value. The processes are highly intertwined with the use of the KMS. It therefore requires consideration of KA across multiple levels and types of knowledge across the entire KM cycle. To achieve this goal, we develop and present a taxonomy of knowledge appraisal practices and discuss their role in the KM lifecycle emphasizing implications for research and practice.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.391

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 studies0.0000.000
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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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