Bibliographic record
Abstract
2015 was a busy year. It saw the passages of Bills C-51 and C-44, which made it easier for the government to gather and share information in the name of security, while limiting disclosure and adversarial challenge in legal proceedings concerned with substantive rights. Less conspicuous developments included a timely evaluation of the Special Advocate Program (SAP) and a finding, by the Immigration and Refugee Board (IRB), that Manickavasagam Suresh is inadmissible to Canada - presumably for reasons relating to national security. I say “presumably” because the finding rested on secret evidence and has not yet been reported. Readers may be familiar with Mr. Suresh by virtue of the famous 2002 case Suresh v. Canada, where the Supreme Court of Canada (SCC) ruled that Canada may deport persons to face the substantial risk of torture under “exceptional” circumstances.
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 | 0.037 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.032 | 0.024 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".