A Comparative Study of Downsizing Strategies applied in the Reforms of China’s State-owned Enterprises
Bibliographic record
Abstract
This paper analyzes two typical downsizing strategies applied in changes of China’s state-owned enterprises based on behavioral science, which are proactive strategy and reactive strategy. It points out the different impacts upon employees from proactive and reactive strategies, using survey and field study methods. Finally, the conclusion is that proactive downsizing strategy is the best one in changes of China’s enterprises nowadays. Key words: downsizing, downsizing strategy, reforms of China’s state-owned enterprises Resume: Cet essai analyse d’abord deux types de strategie de reduction appliques dans le changement des entrepries d’Etat chinoises et qui sont bases sur la science behavioriste, a savoir la strategie preactive et la strategie reactive. Ensuite il indique, par des methodes d’enquete et d’etude sur le terrain, les influences de ces deux strategies sur les employes. Enfin, on aboutit a la conclusion : la strategie de reduction preactive est la meilleure dans le changement actuel des entreprises d’Etat. Mots-Cles: reduction, strategie de reduction, changement des entreprises d’Etat
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".