Selection of P3 delivery methods for Sustainable Social Infrastructure Projects Using the Analytical Hierarchical Process
Bibliographic record
Abstract
This thesis studies the necessary shift in screening practices of public-private partnerships (P3) projects in Canada, moving beyond traditional qualitative criteria to include broader environmental, social, and governance (ESG) project objectives. The current P3 screening, while effective, needs adaptation to align with Canadian societal and environmental infrastructure goals. In response, this thesis focuses on three objectives aimed at improving social infrastructure P3 procurement and promoting sustainable and responsible project management practices for these projects. Firstly, it identifies and describes Canadian-specific ESG criteria important for ensuring responsible sustainability in delivering social infrastructure projects. Secondly, it develops an ESG-PPP screening matrix to evaluate social infrastructure projects based on responsible sustainability thresholds, determining their suitability for P3 procurement. Thirdly, it implements a multi-criteria analysis using the Analytic Hierarchy Process (AHP) to determine the most appropriate P3 model for social infrastructure projects, considering the identified ESG criteria and quantitative value-for-money criterion. The AHP-PPP selection tool is applied to three case studies analyzing two AHP scales to assess their consistency ratios and the reliability of the P3 selection results. The results indicate that the balanced-n scale exhibit lower inconsistency ratios compared to the fundamental AHP scale, and decisions on P3 options remained consistent across all case studies using both scales, suggesting that the Fundamental AHP scale remains reliable if decision-makers accurately reflect the relative importance of P3 options. Overall, this thesis addresses the increasing need for sustainable and responsible management of social infrastructure projects in Canada by integrating ESG factors into the current procurement process.
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.
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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".