Thinking Outside the Black Box: The Legal Status of Emerging Flight Recorder Technologies in Canada
Bibliographic record
Abstract
In Canada, cockpit voice recorders (CVRs) are only mandated for a specific category of aircraft, resulting in many commercial and business aircraft not being equipped with these crucial devices. More recently, industry initiatives have led to the development of new technologies enabling carriers to install lightweight and relatively affordable flight recorders on aircraft that are not currently covered by CVR requirements. Many of these non-conventional recorders have capabilities that meet or even exceed those of conventional CVRs by relying on high-resolution audio and imagery recordings, infinite cloud storage, data links, etc. Canadian legislation bestows a statutory privilege on the contents of CVRs installed in accordance with regulations, but it remains unclear if this privilege also applies to non-conventional flight recorders. Through case studies, this article analyses the design and functionalities of existing devices on the market, revealing that some recorders are not actually CVRs within the meaning of the law and/or suffer from technical vulnerabilities that preclude their contents from being privileged. This article concludes by outlining the resulting liability risks associated with the use of non-conventional recorders and recommends that regulations be issued to approve eligible devices based on a fourprong test, which attests whether their recordings can benefit from the statutory privilege afforded to conventional CVRs.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| 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".