The Super-cycle Mechanism and Model of Enterprise Expansion
Bibliographic record
Abstract
In this paper, as the super-cycle theory of self-organization integrating methodology for enterprise expansion process, we study the super-cycle coupling mechanism of the enterprise and the environment, enterprises and enterprises ,and among factors within the enterprises, then construct the super-cycle model of M & A expansion , virtual enterprise expansion and union expansion to effectively combine resources ,and make synergies spiral under nonlinear . Key words: super-cycle theory, Merger expansion, Virtual enterprise expansion, Union expansion Resume: Dans l’article present, comme la theorie de super-cycle d’auto-organisation integrant la methodologie du processus de l’expansion de l’entreprise, on etudie le mecanisme de liaison de super-cycle entre l’entreprise et l’environnement, entre les entreprises et parmi les facteurs interieurs de l’entreprise, et puis construit le modele de super-cycle de l’expansion M&A, l’expansion virtuelle de l’entreprise et l’expansion d’union afin de combiner effectivement les ressources et faire synergie spirale non-lineaire. Mots-Cles: theorie de super-cycle, expansion de fusion, expansion virtuelle de l’entreprise, expansion d’union
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".