Bibliographic record
Abstract
Citizens deserve to know, and in some cases need to know, what their governments — including their courts — are up to. Governments like to be able to, and in some situations need to be able to, gather information about what “the governed” are up to. In recent times the fear generated by an unknown enemy with unknown resources rightly leaves state authorities anxious to preserve whatever advantage they might enjoy in combating terror. at the end of the day the test of a democratic legal system is not whether it permits secret proceedings, or gives the state the power to discover private information: obviously for any sovereign authority to function in a meaningful way it must be able do these things, at least some of the time. Our focus should be on the procedures in place to require the justification of these two departures from the (unattainable) democratic ideal of the perfectly unintrusive, transparent state I tend to the view that the Supreme Court of Canada has, by and large, struck an appropriate balance in matters related to the flow of information between and about state and individual. The judgments in Tessling and Mann are consistent with the Court’s previous jurisprudence and continue to approach issues of privacy and search in a principled, responsible manner. Section 8 guarantees only a reasonable expectation of privacy, a standard which requires an internal balancing of the state’s interest in the prompt and expeditious investigation of crime against the democratic ideal of an unintrusive government. Too broad a reading of reasonable expectation of privacy runs the risk of creating excessive and unnecessary hurdles to investigations without any significantly increasing the scope of democratically meaningful privacy.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.147 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".