Bibliographic record
Abstract
Extradition—the formal legal surrender between states of individuals sought for criminal prosecution or to serve a sentence—is an essential tool in the worldwide fight against cross-border crime. In a time when the permeability of borders to criminal conduct has reached previously untold levels, the importance of effective international law enforcement cooperation has similarly intensified. Criminal investigation and enforcement powers can, for all practical purposes, only operate within national borders, but criminals themselves are not so constrained. Human trafficking, internet fraud, financial crime, wildlife trafficking—all are running rampant. All states, and their citizens, have a pressing interest in crime suppression, in which extradition plays a key role; in Canada we need only think of the cases of Luka Magnotta, Nicholas Ribic and Gerald Gallant to know that extradition is a process we need; and because criminals are so mobile, we need it to work well. The authors of the articles in this issue have varying perspectives, but all agree on an essential point: Canada’s extradition laws and practices have significant problems that are producing unjust and wrongful extraditions, and must be reformed.
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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.034 | 0.022 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.020 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".