Bibliographic record
Abstract
Since the 1960s, celebrity drug trials have usually involved actors or musicians. The first drug prosecution of a Canadian “celebrity” took place in 1985 after the Royal Canadian Mounted Police (RCMP) found a small amount of marijuana in the luggage of New Brunswick Premier Richard Hatfield at the airport in Fredericton. He was charged with simple possession and, aided by a team of lawyers, pleaded not guilty. Although Hatfield was the most successful premier in the province’s history, he was facing challenges over the economy and language policy, and a finding of guilt would have devastated both his political career and the fortunes of his party. This article examines the Hatfield drug prosecution, which was followed by revelations of drug use with university students in 1981, as a chapter in Canadian legal and political history. It involved not only a privileged defendant, but also the independence of judges, the role of the RCMP, the relationship between the courts and the media, federal-provincial relations and an internal RCMP probe. Hatfield, the political celebrity, won his 1985 court battle but, with his lifestyle impugned, lost in the court of public opinion. In 1987, his party was crushed by the landslide victory of Frank McKenna’s Liberals.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".