SORTING SUBCONTRACTORS’ ACTIVITIES IN CONSTRUCTION PROJECTS WITH A NOVEL ADDITIVE-VETO SORTING APPROACH
Bibliographic record
Abstract
Selection processes in civil engineering infrastructure projects might require more time and effort than the decisionmakers involved in these projects are normally prepared to devote to running them. A novel approach is proposed to sort these activities into classes that represent their impact on the project, namely additive-veto sorting model, which should be considered before any bidding procedure. Therefore, problems regarding the client’s satisfaction caused by subcontractors can be avoided, and the decision-makers involved in the selection problem can devote to each class an effort compatible with the impact that activity might have on the project. The novelty of this method is that it was built to reflect the quasi-compensatory rationality of decision-makers in the construction industry; it provides them with insights on subcontractors’ activities, and it is grounded on and inspired by a real case study. The new parameters proposed within this model introduce the idea of vetoing an activity being assigned to a class when this activity is incompatible with the decision-maker’s preferences. By using this novel method, the authors succeeded in finding results that avoided a complete compensation amongst the factors considered, taking into account ranges that would be of significant importance in the decision process.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".