Project and portfolio management: a multilayer framework to support innovation-driven SMEs in the industry of construction and building materials. Case of Canada
Bibliographic record
Abstract
The concept of building defined approaches, models and frameworks for optimizing different projects that belong to the same portfolio is gaining more attention and emphasis from companies, especially the small and medium sized ones. Yet, many problems that lie into the existing frameworks are keeping those companies away from using it. A sample of these SMEs that work in the field of construction and building materials in Canada were a filed for our research to better identify the issues in these existing frameworks, understand its influence and effect on companies and develop an ideal integrated framework that deals with project, portfolio and innovation management at the same time. The data has led in this research to identify 5 major issues that needed to be embed into the new integrated approach that is called Innoframe. This is a new framework that is an outcome of a thorough study on the usage, behavior and prospects of two main levels of personnel which are team members and their project managers. The study has followed a straightforward path in the sense of researching, analyzing and developing. The approach allowed the study to make good use of the literature and data collected on one hand, and to translate it into useful tools that help a lot in developing the new framework. All in all, the study emphasized the three main phases mentioned previously to come up with a new integrated framework that can serve as a roadmap for SMEs in the industry of construction and building materials.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".