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Record W2081735140 · doi:10.1108/13665620010316000

The competitive advantage of organizational learning

2000· article· en· W2081735140 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 Workplace Learning · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsCompetitive advantageOrganizational learningValue (mathematics)Knowledge managementOrganizational cultureBusinessLearning organizationOrganizational communicationWork (physics)Organization developmentPublic relationsSociologyMarketingPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Aims to understand how training and communication help an organization to learn and gain a competitive advantage. Explores the link between training, communication and measurement with individual and organizational learning by conducting a specific qualitative analysis looking for insights into how the concepts sometimes work and how they fail. Also touches on the general themes that have shaken management and employees over the last 15 years as they struggle to survive and prosper in the global village, and compares this concept with ideas that have been prevalent in organizations since the early 1970s. The objective is to understand how organizations can tap their intangible assets and increase their value to the organization, the individual who holds the knowledge and the society that benefits from a healthy economy.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.189
Teacher spread0.186 · 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