A Cost-Benefit Analysis of the Privatization of Canadian National Railway
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article uses cost-benefit analysis to estimate the welfare gains from the privatization of Canadian National Railway (CN) in November 1995, one of the largest rail privatizations in history. It also shows how these gains have been distributed among consumers, producers, and government, and between Canadians and non-Canadians. The article uses the costs of Canadian Pacific Railway to create a more credible comparison than in previous privatization studies. Based on a conservative counterfactual, we estimate that CN's privatization generated welfare gains of at least $4 billion (in 1992 dollars). However, the welfare gain was possibly as high as $15 billion. The Canadian government captured almost half of these gains, while CN shareholders captured most of the rest.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| 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 it