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Towards a Learning Organization

2021· book-chapter· en· W3187423678 on OpenAlexaff
Anindita A. Bose, Colin Furness

Bibliographic record

VenueAdvances in knowledge acquisition, transfer, and management book series/Advances in knowledge acquisition, transfer and management book series · 2021
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKnowledge managementSocial learningLearning organizationOrganizational learningOrganizational changeAdaptive capacityComplex adaptive systemPsychologyComputer sciencePolitical sciencePublic relationsArtificial intelligence

Abstract

fetched live from OpenAlex

A learning organization is one that is consistently capable of adaptive change in response to signals from its environment. However, knowledge management initiatives to enact learning organizations have not been uniformly successful. This chapter focuses on the role of the psychological environment of the individual in enabling or hampering organizational learning. Six theories drawn from multiple fields are reviewed to identify both opportunities and barriers to fostering change at the level of the individual. These include orientation to learning, motivation to act, and capacity for change. However, the authors argue that organizations ought to be regarded as complex social systems. Change strategies intended to foster a learning organization are more likely to succeed if they embrace the idea that designing change for complex social systems requires a special approach: design thinking. This is characterized by iterative prototyping, experimenting, trialing, and piloting changes to work processes, structures, and tasks.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.015
Open science0.0010.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations1
Published2021
Admission routes1
Has abstractyes

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