Construction of the Capability Maturity Model of Dynamic Strategic Cost Management on Real Estate Development Enterprises
Bibliographic record
Abstract
Real estate development enterprises are in the process of gradually mature, and its dynamic strategic cost management is also from generation to development gradually mature process. How to effectively grasp the real estate development enterprise dynamic strategic cost management stage, and look for the pushing direction and key process of evolution of dynamic strategic cost management of real estate development enterprise. There is still a lack of standardized procedures and methods. The capability maturity model can reveal the characteristics of the things that are in the process of the development, in line with the characteristics of real estate development enterprise dynamic strategic cost management and construction requirement. Therefore, this paper establishes the dynamic strategic cost management capability maturity model of real estate development enterprises from seven aspects: customer, market, product, process, organization, technology, government and society, which combined the framework of Capability Maturity Model with the actual characteristics of the real estate development enterprise dynamic strategic cost management. It studies the properties and characteristics of each grade real estate development enterprise dynamic strategic cost management, and analysis of low grade to high grade key process area evolution, to provide theoretical and practical guidance for the dynamic strategic cost management in real estate enterprise
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".