A Test on the Systematic Fuzzy Measurement of the Key degree for the Core Employees’ Performance Traits
Bibliographic record
Abstract
According to the employees’ embedded performance view of organizational social capital and its theoretic analytic dimensions, the core knowledge employees’ key performance traits are operationally defined as key degree with seven measuring dimensions. A scale for measuring the key degree is developed and empirically analyzed by means of questionnaire with Cronbach’s α >0.7 and significant three-factor-structure validity. And then systematic fuzzy decision theory is introduced to comprehensively measure the key degree indicator (KDI) of the core employees’ performance traits with the third party test, and the results initially show satisfied discriminating rate of the core employees (94%) from the sample and significant correlation between KDI and the organizational innovation performance (R=0.82, P 0.7 de Cronbach et la validite de la structure de trois facteurs signifiante. Puis la theorie de decison systematique et floue est introduite afin de mesurer le degre cle indicateur (key degree indicator /KDI) des traits de la performance des employes importants avec la troisieme partie de test, et les resultats montrent au debut un taux de distinction satisfaiseant des employes (94%) dans un echantillon et une correlation signifiante entre KDI et la performance d’innovation organizationnelle(R=0.82, P<0.001), et la prediction potentielle de la performance d’innovation organizationnelle avec KDI . Mots-Cles: employes importants, trait de performance, mesure systematique et floue
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".