Public-private partnerships practice in the infrastructure development: The case study of Richmond-Airport-Vancouver (RAV) rail line development in Vancouver, British Columbia, Canada.
Bibliographic record
Abstract
In Canada, public-private partnerships (PPPs) are applied in many sectors such as infrastructure, healthcare, education, welfare, technology, environment, criminal justice (Bain 2010: 13; Siemiatycki 2006: 137; Urio 2010: 5). Regarding the PPPs application in infrastructure sector in Canada, the government employs Design Built Transfer Operate (DBFO) scheme of PPPs to build Richmond-Airport-Vancouver (RAV) rail line (Siemiatycki 2006: 138). It was evident from the Siemiatycki’s case (2006) that RAV rail line project using DBFO model of PPPs has not provided a transparent information regarding the initial processes. This essay, therefore, aims to critically discuss the reason for the non-transparent processes of public-private partnerships in the infrastructure development of urban public transit in Vancouver, British Columbia, Canada.
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How this classification was reachedexpand
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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; both teacher heads agree on what is shown here.
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".