Book Review of <i>Retiring the Crow Rate: A Narrative of Political Management</i> by Arthur Kroeger.
Bibliographic record
Abstract
It was the Crowsnest Pass Agreement in 1897 between the Canadian Pacific Railway (CPR) and the federal government that came to establish the freight rate structure for export grain. When the rates were made statutory in 1925 they remained fixed until 1983, when the Western Grain Transportation Act (WGTA) replaced the Crowsnest Pass Agreement. The fixed-rail freight rate generally provided sufficient revenues for the two major railways, the CPR and the Canadian National Railway (CNR), to develop a network of branch lines of over 19,000 miles of track designed for horse and wagon technology. After 1960, when rail costs of moving grain exceeded the revenues, the two railways refused to maintain and upgrade the rail transportation system. At first the federal government with provincial government help provided subsidies to offset the railways’ losses and maintain the rail system. But by the mid-1970s the federal government, believing it could no longer continue to subsidize an overbuilt and inefficient rail system where freight rates were fixed, set in motion a series of studies and task forces eventually leading to a new set of rail transportation policies that affected freight rates and the rail network. It is the story of how the Crow Rate was changed that Arthur Kroeger recounts in this informative, entertaining, and often humorous book.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".