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Record W4391602831 · doi:10.4018/ijkm.336925

Knowledge Management Practice and Organizational Performance in the Context of International Schools

2024· article· en· W4391602831 on OpenAlexaff
T.B.C. da Silva, Nuša Fain

Bibliographic record

VenueInternational Journal of Knowledge Management · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsCarleton University
Fundersnot available
KeywordsContext (archaeology)Knowledge managementOrganizational learningBusinessComputer scienceHistory

Abstract

fetched live from OpenAlex

This exploratory study delves into three private international schools' knowledge management (KM) practices amidst the Covid-19 pandemic. Utilizing Gold et al.'s KM framework, the research examines the applicability of this model in the unique industrial context of international schools and explores the potential influence of KM on organizational performance within this context. The findings highlight that teachers proficient in articulating KM processes and infrastructure perceive their schools as more successful in the international education landscape. As schools transition back to in-person learning, leaders and stakeholders are urged to evaluate their KM practices and consider targeted initiatives to cultivate specific competencies, fostering a sustainable competitive advantage and enhancing both financial and non-financial firm performance. This study's originality lies in examining KM practices in international schools during the pandemic, stimulating conversations among school leaders and stakeholders to optimize knowledge management in the post-pandemic era.

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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.007
Scholarly communication0.0090.004
Open science0.0010.006
Research integrity0.0010.002
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 head0.020
GPT teacher head0.335
Teacher spread0.315 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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