Contractual incentives between the main contractor and third parties for better performance in a Bouwteam
Bibliographic record
Abstract
In the last decade, the Bouwteam concept has become increasingly popular in the construction industry. A Bouwteam agreement is a bilateral agreement between a client and a main contractor, in which the main contractor is involved at an early stage. An advantage of a Bouwteam collaboration is that risks which occur in the construction phase are mitigated in the design phase. In practice, both the client and the main contractor can employ third parties to transfer parts of their design responsibilities. In the recent Bouwteam models, there is the possibility that a substantial part of the design responsibility is passed on to the main contractor and his third parties. These third parties have an increasing influence on the achievement of project goals, which are usually the design quality, design costs, design planning and client satisfaction. Hence, this research is focused on the implementation of contractual incentives for the designing third parties, who are employed by the main contractor in a Bouwteam. The contractual incentives are used to influence the performance of these third parties, in such a way that Bouwteam goals are achieved.
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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 | 0.034 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".