Advancement of conventional cost benefit for selection of truly sustainable infrastructure alternatives
Bibliographic record
Abstract
In the 21st century, selection of a best infrastructure alternative became prominent for all public sector projects. Initially, such selection was using the well-established for assessment of most profitable private investments, the cost-benefit approach. Criticized for insufficient inclusion of project social and ecological effects, this approach was later replaced with variety of multi-criteria-based methods. An overview of both approaches identifies their advantages and potential burdens for fair assessment of economical, social, and ecological effects. All analyses are supported by world-wide practical examples with emphasis on historical tunnelling projects from Greater Toronto (Canada). Relying on some findings by Canadian and Australian scholars and the results of their own research, the authors develop an enhancement to the conventional cost-benefit approach to ensure selection of fact-proven most sustainable alternatives. As demonstrated, application of this methodology can reduce infrastructure planning timeline, also working toward its better sustainability and helping with achievement of the United Nations’ Sustainable Development Goals.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".