Model for developing trust on US construction projects
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
Purpose The purpose of this paper is to identify the factors found on US construction projects that are perceived by contractors to strengthen or weaken trust between contracting stakeholders and to develop a framework for evaluating these relationships. Design/methodology/approach A comprehensive framework containing a number of factors (54) that could impact trust on construction projects was first developed. A survey questionnaire was then developed and administered via phone to contractors selected from the Engineering News Record top 400 US construction companies. The survey findings were then used to develop a trust model and case studies were used to validate and revise the trust model. Findings A trust model is developed that helps large US contractors measure and improve trust with other stakeholders on their projects. Practical implications Large US contractors are now provided with a tool not previously available to help them measure and improve trust between the different contracting parties on construction projects which can help them decrease project time and costs, and improve project results. Originality/value The proposed trust model adds a number of different dimensions to the existing trust models found in the literature and as such improves the contractor’s ability to foster and enhance trust on a US construction project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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