Assessing Infrastructure Project Innovation Potential as a Function of Procurement Mode
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Bibliographic record
Abstract
The delivery of infrastructure projects as long-term capital investments is impacted in most cases by critical issues of budget constraints, program delays, quality and safety concerns, and an increasingly complex stakeholder environment. Innovation, as it relates to the physical, process, organizational/contractual, and financial/revenue dimensions of a project, has a central role to play in not only contributing to the requirements set for a wide variety of project performance metrics but also improving upon them. Proposed in this paper is a theory in the form of a set of factors (drivers/inhibitors to innovation) and related state values that influence the potential for the identification and adoption of innovations that improve project efficiency or offer increased value. This theory is embedded in a supporting assessment framework to assist with selecting and structuring a project’s procurement mode to enhance the innovation potential of a project from the perspective of a government agency tasked with such decisions. The framework was developed in response to a lack of tools to help practitioners with tasks relating to innovation assessment, especially in regard to conducting a public sector comparator analysis when a public–private partnership procurement mode is being considered among others; and aligning terms and conditions in bid documents, requests for proposals, and concession agreements in a way that fosters beneficial innovations to the extent that factor states can be controlled. The framework provides the project evaluation process with a means of assessing project innovation potential at the very front end of the procurement mode selection process, and is meant to be comprehensive yet simple and easy to use in practice. It can also be used by researchers to help analyze on a postproject basis reasons why innovations were or were not adopted for a specific project context. The framework is applied to case studies on two infrastructure projects in Scandinavia and the United States to demonstrate its application and to assess the role that choice of procurement mode had in influencing the innovations used.
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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 | 0.003 | 0.002 |
| 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