The construction industry macro maturity model (CIM3): theoretical underpinnings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce an alternative approach of measuring construction industry performance using maturity modeling. The focus is on introducing a newly developed maturity model referred to as the construction industry macro maturity model (CIM3) and highlighting its use by assessing the maturity of the construction industry of the Province of New Brunswick, Canada. Design/methodology/approach Current methods of construction industry performance measurement such as labor productivity and competitiveness are briefly reviewed, highlighting their weaknesses. The theoretical underpinnings of the CIM3 are discussed and the implementation of the CIM3 to measure the cost and quality management maturity of the New Brunswick construction industry is presented. Findings An assessment of the construction industry's maturity using the CIM3 provides a leading indication of performance. This is based on the industry being structured according to key practices areas that contain key practices. The industry's key practices are linked to objectives that lead to the achievement of performance goals. The maturity of the construction industry with respect to its key practices is a function of the relative importance of the key practices and the capabilities of the industry in implementing the key practices. Based on this, the implementation of the CIM3 in New Brunswick found that the NB construction industry is more mature in cost management than in quality management. Originality/value This paper contributes to the existing body of knowledge on industry performance measurement, and more particularly, construction industry performance measurement. The concept of maturity modeling applied here promotes and demonstrates the use of leading indicators of performance, as recommended in most performance measurement literature.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it