Bibliographic record
Abstract
When governments announce that they are going to spend vast sums of taxpayers’ money on a new public infrastructure project, you can be certain they will praise all the terrific new benefits that the project will bring to citizens, making everyone’s life easier, safer, greener and better. But this does not tell us whether we are better off as a society, after accounting for the cost of these projects borne by taxpayers today and well into the future. In reality, there is a meaningful risk that a project undertaken without a proper business case could end up making citizens’ lives worse. That new commuter train might look sleek and shiny and seem convenient for some, but a close business case analysis of recent transit projects in Canada’s three largest cities suggests that in as many as four cases out of 21 projects, the burden of paying for the projects does not justify the public investment. In a review of thirteen recent public transit projects in the Greater Toronto and Hamilton Area (GTHA), at least three projects had benefits that fell short of the costs. Yet, all three projects went ahead (or have been funded). Only one project showed large net benefits for citizens once all considerations were accounted for. Three projects showed small net benefits – of a size that can be easily offset by a modest cost over-run. The six remaining projects did not have any publicly available business cases. In the Greater Montreal area, a review of three recent major transit projects turned up no evidence of a publicly available business case for any of them. As a result, Montrealers are in the dark as to how much benefit or value destruction the three projects are responsible for. Things are far more encouraging in Vancouver, however, where three out of the five major transit projects undertaken or funded in recent years were backed by business cases showing a net benefit. Only one project did not show a net benefit and one project did not have a business case. Of course, business cases only make projections about net benefits. Rarely, if ever, do governments undertake an ex post review to determine whether their estimates were correct and if the project has delivered — or destroyed — the value expected. Given that these projects can run into the billions of dollars, tie up immense amounts of government resources, and can cause any number of disruptions to business and families, it is remarkable how little cost-benefit scrutiny is brought to bear on them. Without these ex post business cases, there can be no lessons learned from past projects. There can be no assurance that we can make better investment decisions going forward.
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 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 | 0.003 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".