Collaboration through innovation: implications for expertise in the AEC sector
Bibliographic record
Abstract
Collaboration is key for successful delivery of building projects in the Architecture, Engineering and Construction (AEC) sector. Innovative project delivery approaches developed over the past two decades envision new ways of collaborating and specifically aim at improving the performance of and value generated by this key economic sector. Collaboration, however, remains an ill-defined and highly amorphous concept. This makes it difficult to investigate and consequently develop a body of knowledge, which is central to defining a field of expertise in this area. The aim of this investigation is to explore the notion of an expertise in collaboration in the AEC sector and the implications of these innovative project delivery approaches on this expertise. The concept of collaboration is developed across five core entities: structure, process, agents, artefacts and context. These entities are then framed through a critical realist lens to lay the groundwork for a body of knowledge of collaboration in the AEC sector. The impact of the current shift to these innovative approaches is investigated within this framing. The findings set a course of action to develop a body of knowledge and a field of expertise on collaboration in the AEC sector.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".