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Record W2038097592 · doi:10.1108/13665620310488575

Organisational learning in a public sector organisation: a case study in muddled thinking

2003· article· en· W2038097592 on OpenAlexfundno aff
J. David Betts, Rick Holden

Bibliographic record

VenueJournal of Workplace Learning · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsnot available
FundersMcGill University
KeywordsPublic sectorOrganizational culturePublic relationsLearning organizationOrganisational changeSociologyPower (physics)Workplace learningKnowledge managementBusinessPolitical scienceWork (physics)EngineeringComputer science

Abstract

fetched live from OpenAlex

Organisational learning practice within the public sector is relatively under researched. This paper draws on case study data from a local authority committed to the creation of a “learning organisation” culture; data generated through the evaluation of two programmes implemented as part of this strategic objective. The article contends that tensions between the need to deliver specific improvements in the organisation and the desire to encourage creative innovation led to an uncertainty surrounding the most appropriate model of learning to pursue the broader goal. Both programmes exposed tensions between opportunities for individual growth and traditional values which constrained that growth beyond the individual. The article concludes that for organisational learning in the public sector to be effective it must be collective, processual and above all cognisant of organisational power patterns.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0230.014
Scholarly communication0.0060.003
Open science0.0030.007
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.034
GPT teacher head0.240
Teacher spread0.206 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations41
Published2003
Admission routes1
Has abstractyes

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