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Record W2084100920 · doi:10.1108/eum0000000005482

The knowing organization as learning organization

2001· article· en· W2084100920 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

VenueEducation + Training · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSensemakingKnowledge managementContext (archaeology)Organizational learningKnowledge value chainPersonal knowledge managementOutcome (game theory)Interpretation (philosophy)Action (physics)Computer science

Abstract

fetched live from OpenAlex

Examines the information processes that support organisational sense‐making, knowledge creation and decision making. Sense‐making involves interpreting the raw data of the environment by enactment, selection and retention. New knowledge is created by knowledge conversion, knowledge building, and knowledge linking. Completely rational decision making would involve identifying alternatives, projecting the outcomes of each alternative and evaluating the alternatives and their outcomes according to known preferences and objectives. In the organisational knowing cycle, a continuous flow of information is maintained between sensemaking, knowledge creating, and decision making, and the outcome of information use in one mode provides the elaborated context and the expanded resources for information use in other modes. An illustration is given of a knowledge cycle in the World Health Organisation Smallpox Eradication Programme in which continuous cycles of interpretation, innovation and adaptive action underpinned the success of the project.

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)0.003
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Insufficient 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.130
GPT teacher head0.404
Teacher spread0.274 · 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