Bibliographic record
Abstract
The increasing role of "special advocates" in common law jurisdictions raises fundamental questions about the development of the law in response to new challenges and the extent to which individual rights can be abrogated in the name of national security. Special advocates are employed to examine and challenge classified evidence, withheld from affected persons and their legal advisors, in closed proceedings. They are, notionally, representing the affected person, but face an almost complete restriction on communication once exposed to the classified evidence. This is strikingly at odds with long-established norms of advocacy and a fair hearing, leading the United Kingdom Joint Committee on Human Rights to describe the system as "Kafkaesque". The special advocate function, widely utilised in the United Kingdom, will be statutorily introduced into New Zealand with the passing of the Immigration Bill 2007, mirroring a similar development in Canada. The Bill extends the use of classified information in immigration decision-making and allows for special advocates to examine and challenge classified evidence in review, appeal or detention proceedings. That Bill is the subject of this article.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.035 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 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".