Bibliographic record
Abstract
The nearly three decades in which Beverley McLachlin was a member of the Supreme Court, including 18 as Chief Justice, witnessed a number of shifts in Canadian penal policy and in the reach and impact of criminal law. During the Harper decade (2006 to 2015) in which the federal Conservatives enjoyed a majority government led by Prime Minister Stephen Harper, criminal justice policy took a turn toward the punitive. The federal government tore a page out of the American legislative handbook and sought to “govern through crime”, albeit in a more restrained Canadian style. Criminologists Anthony Doob and Cheryl Webster have posited that pre-Harper, Canadian criminal justice policy was grounded in four pillars that enjoyed support across party lines. These pillars were that social conditions matter; that harsh punishments do not reduce crime; that the development of criminal justice policies should be informed by expert knowledge; and that changes in the criminal law should address real problems. These principles were cast aside, Doob and Webster argue, beginning at least in 2006 with the passage of numerous crime bills that, to name just a few, created new crimes with enhanced penalties; proliferated mandatory minimum sentences; reduced the availability of conditional sentences served in the community; made it easier to have someone declared a dangerous offender (and therefore, imprisoned indefinitely); removed opportunities for early parole; and more.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".