Study on the Essences of the Identification of Core Competence
Bibliographic record
Abstract
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 Résumé Sur la base de la discussion sur les caractéristiques de la compétence fondamentale de l’entreprise, l’auteur signale les classifications et les imperfections du jugement de la compétence fondamentale de l’entrprise tant à l’intérieur qu’à l’extérieur du pays. L’étude montre que l’essence du jugement de la compétence fondamentale de l’entreprise consiste à la mettre en valeur. Mots-clés: la compétence fondamentale de l’entreprise , le jugement , mettre en valeur 摘 è¦ åœ¨è¨Žè«–ä¼æ¥æ ¸å¿ƒèƒ½åŠ›ç‰¹å¾µçš„基礎上，指出了目å‰åœ‹å…§å¤–ä¼æ¥æ ¸å¿ƒèƒ½åŠ›è˜åˆ¥çš„分類åŠå…¶ç¼ºé™·ã€‚ç ”ç©¶èª ç‚ºä¼æ¥æ ¸å¿ƒèƒ½åŠ›è˜åˆ¥çš„本質是ä¼æ¥æ ¸å¿ƒèƒ½åŠ›æŒ–掘。關éµè©žï¼šä¼æ¥æ ¸å¿ƒèƒ½åŠ›ï¼›è˜åˆ¥ï¼›æŒ–掘
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".