Competence Set Expansion Decision-making Analysis Based on Important Degree Coefficient
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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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".