Criminal Justice Policy during the Harper Era: Private Member’s Bills, Penal Populism, and the Criminal Code of Canada
Bibliographic record
Abstract
Abstract During the 2015 Maclean’s election debate, Stephen Harper commented, “we have more private member’s legislation that has gone through Parliament under this government than multiple governments before us.” This statement is borne out by empirical evidence: more private member’s bills (PMBs) have become law during Harper’s time in government, compared with most previous parliaments. However, PMBs are subject to less analysis than government bills and do not receive legal scrutiny by the Department of Justice, potentially implicating the protection of rights. Moreover, while one might assume that PMBs concern innocuous local and/or specialized interests, many Harper era PMBs effect substantive legal change to national issues like criminal justice policy. This paper examines the law and order trend in PMBs and addresses the following: why would the PMO under Stephen Harper, noted for its centralized control over all aspects of public policy, permit backbench MPs a role in criminal justice policy, through PMBs?
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".