Internet Surveillance, Regulation, and Chilling Effects Online: A Comparative Case Study
Bibliographic record
Abstract
With internet regulation and censorship on the rise, states increasingly engaging in online surveillance, and state cyber-policing capabilities rapidly evolving globally, concerns about regulatory "chilling effects" online—the idea that laws, regulations, or state surveillance can deter people from exercising their freedoms or engaging in legal activities on the internet have taken on greater urgency and public importance. But just as notions of "chilling effects" are not new, neither is skepticism about their legal, theoretical, and empirical basis; in fact, the concept remains largely un-interrogated with significant gaps in understanding, particularly with respect to chilling effects online. This work helps fill this void with a first-of-its-kind online survey that examines multiple dimensions of chilling effects online by comparing and analyzing responses to hypothetical scenarios involving different kinds of regulatory actions—including an anti-cyberbullying law, public/private sector surveillance, and an online regulatory scheme, based on the Digital Millennium Copyright Act (DMCA), enforced through personally received legal threats/notices. The results suggest not only the existence and significance of regulatory chilling effects online across these different scenarios but also evidence a differential impact—with personally received legal notices and government surveillance online consistently having the greatest chilling effect on people's activities online—and certain online activities like speech, search, and personal sharing also impacted differently. The results also offer, for the first time, insights based on demographics and other similar factors about how certain people and groups may be more affected than others, including findings that younger people and women are more likely to be chilled; younger people and women are less likely to take steps to resist regulatory actions and defend themselves; and anti-cyberbullying laws may have a salutary impact on women's willingness to share content online suggesting, contrary to critics, that such laws may lead to more speech and sharing, than less. The findings also offer evidence of secondary chilling effects— where users' online activities are chilled even when not they, but others in their social networks receive legal processes.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".