Search Engines and Global Takedown Orders: Google v Equustek and the Future of Free Speech Online
Bibliographic record
Abstract
The Supreme Court’s decision in Google v Equustek (2017) to uphold a global content takedown order remains controversial and consequential to wider debates about governing the internet. This commentary examines the Court’s underlying assumption – a common view in takedown jurisprudence – that where a portal directs a critical mass of users to a harmful site, it facilitates harm and no longer engages in valuable speech. This ran contrary to the Court’s more considered view of links in Crookes v Newton (2011) as a form of mere reference and valuable per se for enabling the internet as a public forum. This commentary argues the Court should have applied its theory from Crookes to search engine links as no different in principle from others, while conceding that, at scale, links that merely refer can facilitate harm. Drawing on the Copyright Act and the Manila Principles on Intermediary Liability, the author proposes a test for takedown orders that strikes a better balance between free speech and private interests.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.026 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".