Bibliographic record
Abstract
In Google v. Equustek, the Supreme Court of Canada ordered Google to delist all websites used by Datalink, a company that stole trade secrets from Equustek, a Canada-based information technology company. Google had agreed to do so in part, but with respect to searches that originated from google.ca only, the default browser for those in Canada. Equustek however, argued the takedowns needed to be global in order to be effective. It thus sought an injunction ordering Google to delist the allegedly infringing websites from all of Google's search engines—whether accessed from google.ca, google.com, or any other entry point. Google objected. The Canadian Supreme Court, along with the two lower Canadian courts that considered the issue, sided with Equustek (para. 54). The ruling sets up a potential showdown between Canadian and U.S. law and raises critically important questions about the appropriate geographic and substantive scope of takedown orders, the future of free speech online, and the role of intermediaries such as Google in preventing economic and other harms.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".