Moving from production to services: A built environment cluster framework
Bibliographic record
Abstract
The construction industry is no longer focused on providing a single product — i.e. a building or a physical infrastructure, but a variety of services and improvement to the human environment. Major trends such as Performance-based Building as well as Sustainable Build Environment are calling for major changes. These changes mean additional roles for the industry as well as the need for new indicators to measure its performance and its economic impact. This paper proposes a new approach based on the development of a framework for the analysis of the entire construction and property sector — the ‘built environment cluster’. It extends the analysis of an international study based on nine countries—Australia, Canada, Denmark, France, Germany, Lithuania, Portugal, Sweden, and the United Kingdom. The need for improving statistical data is stressed particularly in the context of enlarging the scope of the industry. This new approach provides an excellent starting point for developing new performance indicators that will take into account the changing nature of the industry, for an integrative perspective providing a basis for strategic management, for studying sustainable development in construction and for understanding innovation processes and changes. A comprehensive perspective of the industry performance is crucial for policy initiatives as well as for strategic analysis for firms. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Strategic Property Management is the property of International Journal of Strategic Property Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".