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Record W4388000288 · doi:10.1093/icc/dtad055

Internal versus external knowledge sourcing of organizational rules: an exploratory study of CPGs in a healthcare organization

2023· article· en· W4388000288 on OpenAlexaffabout
Kejia Zhu, Martin Schulz

Bibliographic record

VenueIndustrial and Corporate Change · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Waterloo
Fundersnot available
KeywordsKnowledge managementBounded rationalityRule-based systemProcess (computing)Computer scienceDecision ruleBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In this study, we examine how organizational rules source knowledge. By knowledge sourcing of a rule, we mean the formation of reference ties from the rule to knowledge sources located outside of the focal rule. Rules can source knowledge from sources within the organization (e.g., other rules) and outside (e.g., research publications, policies, standards, etc.). Our theoretical model proposes that knowledge sourcing of rules is driven by inherent incompleteness of rules as a result of bounded rationality of rule makers and rule making process. Incomplete rules can lead to experiences of insufficient rule knowledge, termed “knowledge gaps,” which are shaped by rule dynamics at the levels of individual rules, the rule system, and rule networks. Our theoretical model leads to several hypotheses that we test with longitudinal archival data of clinical practice guideline (CPG) changes in a Canadian healthcare organization. The findings support our theoretical model of incomplete organizational rules which encounter knowledge gaps and close them through internal and external knowledge sourcing. The theoretical and practical implications of the findings are discussed.

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)0.000
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 score0.050
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.226
GPT teacher head0.280
Teacher spread0.055 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes2
Has abstractyes

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