Bibliographic record
Abstract
The time of globalization has seen an onslaught of criminal activity that crosses borders. The legal suppression and prosecution of transnational and cross-border crime raise unique and complex legal issues, and law enforcement, lawyers, and judges have struggled to keep up. Transnational & Cross-Border Criminal Law: Canadian Perspectives fills a pronounced gap in Canadian legal literature. Written by subject matter experts, each chapter exposes and analyzes a current and pressing issue in this realm and is designed both to serve as a resource for researchers and to provide cutting-edge insight on front-burner issues. The group of authors — made up of prosecutors, defence lawyers, government counsel, academics, and civil society advocates — take on a variety of subjects, including terrorism, financial crime and corruption, jurisdiction, extradition, money laundering, trafficking, maritime enforcement, cross-border evidence-gathering, and the international transfer of prisoners. This unique collection will help to advance general understanding of one of the most pressing public policy issues of our time.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.033 |
| Science and technology studies | 0.034 | 0.021 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".