Bibliographic record
Abstract
After graduating from the University of Saskatchewan's College of Law, Jackett was chosen as a Rhodes Scholar. He returned to Canada from Oxford not long before the outbreak of World War II and joined the ten-man Department of Justice as a junior lawyer. Through extraordinary hard work, rigorous legal analysis, and a bent for organization, he eventually became Canada's eighth deputy minister of Justice. He left this position after three years to become general counsel for the Canadian Pacific Railway and was later appointed president of the Exchequer Court of Canada. He quickly revamped the level of service provided by the court to the legal profession and the public and was instrumental in both the creation of the Canadian Judicial Council and the design and creation of the new Federal Court of Canada. As the first chief justice of the Federal Court, he led the new court by example, moulding it into the most efficient and effective court in the country, despite opposition from provincial superior courts and the Supreme Court of Canada. After fifteen years on the Bench he retired in 1979 at the height of his judicial career, believing that this would help the Court develop. He continued to work in relative obscurity at what he loved best - solving legal problems - but never again appeared before the courts.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.021 |
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".