Competence Set Expansion Decision-making Analysis Based on Important Degree Coefficient
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.
Bibliographic record
Abstract
The talented person competence is cultivated and expanded to the actual requisite competence set that has many competence subsets，then carrying on the arrangement of these many competences subset according to its important degree coefficient for providing powerful basis to get the optimal expansion process of expanding from the obtained competence set Sk(E) to the actual requisite competence set Tr(E)．This article uses the fuzzy thought to get various competences subset important degree coefficient in the actual requisite competence set Tr(E)． Key words: Expansion of competence set, Important degree coefficient, Decision analysis Resume: La competence douee de personne est cultivee et etendue a l’ensemble requis reel de competence qui comprend beaucoup de sous-ensembles. On procede ensuite a la gestion de ces sous-ensembles de competence selon leur coefficient de degre important pour fournir la base puissante, dans le but d’obtenir le processus d’expansion optimal de l’ensemble obtenu de competence Sk(E) a l’ensemble requis reel de competence Tr(E). Le present article utilise des pensees brouillees pour obtenir le coefficient de degre important de l’ensemble de competences variees dans l’ensemble requis reel de competence Tr(E). Mots-Cles: expansion de l’ensemble de competence, coefficient de degre important, analyse de decision
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it