Bibliographic record
Abstract
About 18 times a year, E. Hunter Harrison meets with a group of 20–24 management people for three days. They go off-site, turn off their cell phones and pagers, and just focus on a few topics. These retreats, called Hunter Camps, are another example of Hunter's visible support and sponsorship for the new culture at Canadian National Railways (CN). He began the camps in 2003 to communicate his Precision Railroading model to leaders in CN's Transportation Department. He soon added the Five Guiding Principles and how to use them in running the business, and his audience began to broaden to other CN departments. CN added people from every function companywide such as Operations, Finance, Information Technology, Human Resources, and Sales. Over time, four camps a year blossomed to 18, with a plan to reach 1,800 leaders within CN. With the success of the camps, CN expanded the camps to include a broad cast of characters, including CN's customers, union leaders, and recently even leaders from competing rail lines.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.245 | 0.067 |
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".