Exploring association rules overseeing subcontractor quotation behaviors: an empirical study in the construction industry of Taiwan
Bibliographic record
Abstract
Two construction contractual parties enter negotiations to delineate terms and specifics, each aiming to negotiate the quotation to agree on a contract. The research objective is to delineate association rules overseeing subcontractors’ pricing behaviors. Through a comprehensive approach encompassing literature review and expert interviews, this study derives the attributes of quotation behaviors that inform the data collection process. The dataset encompasses information from nine significant engineering categories representing typical construction projects, featuring six quotation behavior characteristics. A total of 10433 quotations sourced from private sector endeavors over the past 10 years have been analyzed. Employing the Apriori algorithm, each engineering category yields between 2 and 5 association rules. A prevailing finding suggests that subcontractors across all sub-work types typically provide discounts of less than 10%, while their profit margins remain relatively stable across different work packages. The frequency of quotations shows considerable variability based on procurement quantity.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".