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.
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 | 0.000 | 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) | 0.049 | 0.019 |
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; both teacher heads agree on what is shown here.
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".