Bibliographic record
Abstract
This chapter introduces ‘The Construction Commons’ as a novel economic collaboration model in construction, rooted in New Institutional Economics and inspired by Elinor Ostrom’s work on common-pool resources. While much research for economic collaboration in construction is informed by game theory, new institutional economics advocates for a more comprehensive approach that includes social, legal, and institutional factors influencing economic activities. The construction commons concept is characterized by a shared financial resource pool, pluralistic decision-making, and collective financial outcomes, aiming to steward collective project resources effectively. The chapter elaborates on new institutional economics, highlighting its relevance to construction management through an emphasis on transaction costs, contractual arrangements, and property rights. It then describes the construction commons in detail, drawing parallels with natural resource management and emphasizing the importance of shared ownership, democratic governance, and sustainable resource allocation. Through a short case study of an Integrated Project Delivery project in Vancouver, Canada, the chapter illustrates the practical application of the construction commons principles, showcasing how they foster shared ownership, transparency, and equitable outcomes. The chapter concludes by exploring the potential of blockchain technology in scaling and governing construction commons, suggesting a promising future for decentralized and digital collaborative project delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".