Bibliographic record
Abstract
Since 2001, governments in Canada and the United Kingdom appear to have increasingly sought to use secret evidence in proceedings against individuals suspected of posing a security threat, relying on the courts to review and legitimate executive claims in closed proceedings. Yet, in the face of secret evidence, adjudicative decision-making is subject to several extraordinary weaknesses. First, the judge is precluded from hearing additional information that can come to light only if the individual or the public is aware of the executive's claims. Secondly, courts are uniquely reliant on the executive to be fair and forthcoming about confidential information and to characterise accurately the case for secrecy. Thirdly, the dynamic or atmosphere of closed proceedings may condition a judge to favour unduly the security interest over priorities of accuracy and fairness. Even where the use of secret evidence is not deemed to be irreparably unsafe or unfair, therefore, its admissibility must be premised on the acknowledgement and careful consideration of corresponding weaknesses in adjudication.
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 | 0.584 | 0.736 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.013 | 0.052 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.013 | 0.020 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".