A Revised Optimal Spanning Table Method for Expanding Competence Sets
Bibliographic record
Abstract
The optimal expansion problem of competence sets can be solves by either mathematical programming method or table based method developed by Feng (2001). Compared to the mathematical programming method, table based method for competence set expansion is a more efficient algorithm in using relevant tableaus to solve the optimal expansion problems. This paper proposes a revised table based method to facilitate developing a computer code. A computer program, called TBM, based on the revised algorithm, was developed to solve the large scale problems of expanding competence sets. A numerical example is given, and some possible future research topics on the related theme are discussed. Keywords: competence set expansion; habitual domains; spanning table methodResume: Le probleme de l'expansion optimale des ensembles de competence peut etre resolu soit par la methode de programmation mathematique, soit par une methode basee sur les tableaux developpee par Feng (2001). Comparee a la methode de programmation mathematique, la methode basee sur les tableaux pour l'expansion des ensembles de competence est un algorithme plus efficace dans l'utilisation des tableaux appropries pour resoudre les problemes d'expansion optimale. Cet article propose une methode basee sur les tableaux revise pour faciliter l'elaboration d'un code informatique. Un programme d'ordinateur, appele TBM, base sur l'algorithme revise, a ete developpe pour resoudre les problemes de l'expansion des ensembles de competences a grande echelle. Un exemple numerique est donne, et quelques sujets possibles de futures recherches sur le theme sont debattues.Mots-cles: expansion des ensembles de competences; domaines habituels; methode de tableau construit
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".