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Study on the Essences of the Identification of Core Competence

2010· article· en· W1938471446 on OpenAlexvenueno aff
HU Shirong, Huang Ding-xuan

Bibliographic record

VenueCross-cultural communication · 2010
Typearticle
Languageen
FieldComputer Science
TopicHigher Education and Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)Core competencyHumanitiesPsychologyPhilosophySocial psychologyManagementEconomics

Abstract

fetched live from OpenAlex

Based on discussion the charactericistic of core competence, the classifications and limitations of identification used in our country or abroad of core competence are pointed out. It is presented that the essence of the identification of core competence is the mining of core competence in this paper.. Key words: core competence, identification, mining Resume Sur la base de la discussion sur les caracteristiques de la competence fondamentale de l’entreprise, l’auteur signale les classifications et les imperfections du jugement de la competence fondamentale de l’entrprise tant a l’interieur qu’a l’exterieur du pays. L’etude montre que l’essence du jugement de la competence fondamentale de l’entreprise consiste a la mettre en valeur. Mots-cles: la competence fondamentale de l’entreprise , le jugement , mettre en valeur 摘 要 在討論企業核心能力特徵的基礎上,指出了目前國內外企業核心能力識別的分類及其缺陷。研究認 為企業核心能力識別的本質是企業核心能力挖掘。 關鍵詞:企業核心能力;識別;挖掘

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.839
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.082
GPT teacher head0.422
Teacher spread0.340 · 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
Published2010
Admission routes1
Has abstractyes

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