The Equustek Effect: A Canadian Perspective on Global Takedown Orders in the Age of the Internet
Bibliographic record
Abstract
This chapter examines the Canadian Equustek case, tracing the development of internet jurisdiction cases in the late 1990s to the current legal battles over the appropriate scope of court orders that wield far greater effect than conventional, domestic-based orders. The chapter begins by recounting the Yahoo France case, the internet jurisdiction case that placed the conflict challenges squarely on the legal radar screen. It continues with a detailed examination of the Equustek decision and its aftermath, including efforts by Google to curtail the effect of the Canadian court order by obtaining a countervailing order from a US court and the use by Canadian courts to extend the ruling to other internet platforms and online issues. It also cites one additional risk with overbroad national court orders related to online activity, namely the prospect of further empowering large internet intermediaries, who may selectively choose which laws and orders to follow, thereby overriding conventional enforcement of court orders and national regulation.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.026 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 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".