You Must Learn to See Life Steady and Whole: Ivan Cleveland Rand and Legal Education
Bibliographic record
Abstract
To understand properly Ivan Rand's views on legal education, just as to understand properly his jurisprudence, it is critical to appreciate that he was born a Victorian and came of age an Edwardian.He came to national prominence in the Atomic Age -in the middle part of the twentieth century -but he was born in 1884, in the midst of the Mahdi's Rebellion in the Sudan and a year before the Riel Rebellion in the old Red River Territory.His birth took place in the same year that the British Parliament enacted the Third Reform Bill, 1 and only two years after the Married Women's Property Act came into force.2 Rand was sixteen when Queen Victoria died and, while old enough to have enlisted in the Canadian battalions that fought in the Boer War, he was too old to serve in the First World War.He was thirty years of age when the "Guns of August" started and almost thirty-five when they fell silent in November 1918.To consider Ivan Rand in this context can seem not a little startling, for we -at least those of us who are not students of the political history of the Maritime Provinces or of the Canadian National Railway -tend to think of him in terms of
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".