WORLD'S BEST WORST TO FIRST IN 10 YEARS : HOW'D THAT HAPPEN? THE SECRET TO CN'S SUCCESS
Bibliographic record
Abstract
This article relates the story of the 10-year turnaround by Canadian National railroad, which had deteriorated into terrible financial shape because it was a crown corporation dedicated to public service about profitability. Having been formed out of several insolvent railroads, it had so much duplicate track that two-thirds of the track together accounted for only 10% of revenues. Under the previous head, progress had been made by cutting work force, shedding ancillary businesses and trimming unprofitable services when the then-Canadian Prime Minister named an outsider, Paul Tellier, to succeed the retiring head in 1992. By 1995, finances were strong enough for the railroad to go private in an offering of 83.8 million shares, which netted $2.2 billion. Tellier gave rail professionals training in business techniques and tightened the connections between operating and financial performance. In 1998, it bought the Illinois Central as the beginning of a southward expansion. Next was a marketing alliance with Kansas City Southern. As CN has continued to grow, it has picked up critics among unions and some customers who complain that staff cuts have hampered service. But overall, CN is experiencing success.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".