SORTING SUBCONTRACTORS’ ACTIVITIES IN CONSTRUCTION PROJECTS WITH A NOVEL ADDITIVE-VETO SORTING APPROACH
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it