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Record W2571120744 · doi:10.22164/isea.v8i4.94

Managing Competency in Non-Profit Organization: Experience with a European University

2014· article· en· W2571120744 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

VenueIssues in Social and Environmental Accounting · 2014
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCore competencyCompetence (human resources)Empirical researchBusinessMarketingProfit (economics)NothingFor profitPublic relationsManagementEconomicsPolitical scienceFinance

Abstract

fetched live from OpenAlex

Competence Management (CM) has been discussed in contemporary academic and practitioner literature as a managing tool of Core Competences. Most of the studies of CM deal with manufacturing sector and profit organizations. Very little is known about CM in services and almost nothing in not-for profit organization. No research report has yet been found in educational institutions. Although, CM is not only important in manufacturing and profit organizations but also important in non-profit, like educational institutions, in order to meet the required quality and competitiveness of 21st century's education. Thus, an attempt has been made in this research to study CM in the administration of one the top ranking University in a Nordic country. The result results reveal that competencies had been defined in individual, network and unit level, but lack of integration of a comprehensive CM framework unable the higher educational institution to achieve the benefits of core competence. Based on the empirical findings, some policy and research directions are given at the end of the research. <br /> <br /><br />

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)0.000
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 score0.034
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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