Enlightenment of GE Culture Change to Chinese Enterprise Culture Construction CHANGEMENT DE LA CULTURE DE GE DES LUMIERES A LA CONSTRUCTION DES CULTURES DES ENTREPRISES CHINOISES
Bibliographic record
Abstract
General Electric company development more than 120 years, has remained focused, become one of the greatest top market capitalization companies in the world. GE created a miracle of global multinational companies, how did GE create miracles? there is no changes in thinking, there is no action for change activities. On the concept of GE management culture change to GE’s success lay a solid foundation, GE conducted a series of institutional culture change, all this will provide plenty of inspiration to the Chinese enterprise cultural construction. Key words : GE; Culture change; Enterprise culture; Motivation Resume Le developpement de la Societe General Electric est plus de 120 ans, est reste concentre, devenu l’un des plus grands premieres capitalisations du marche dans le monde. GE a cree un miracle de societes multinationales mondiales, comment avez-GE creer des miracles? il n’y a aucun changement dans la pensee, il n’y a pas d'action pour le changement activities.On le concept de changement GE culture de gestion a la reussite de GE jeter des bases solides, GE a realise une serie de changement de culture institutionnelle, tout cela va donner beaucoup d’inspiration pour les Chinois entreprise de construction culturelle. Mots cles : GE; Changement de culture; Culture d’entreprise; Motivation
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".