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Record W2186325542

Construction of the Capability Maturity Model of Dynamic Strategic Cost Management on Real Estate Development Enterprises

2015· article· en· W2186325542 on OpenAlexvenueno aff
Zhijun Chen

Bibliographic record

VenueCanadian social science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsProcess managementBusinessMaturity (psychological)Real estateReal estate developmentProcess (computing)Industrial organizationComputer scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.283
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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