Bibliographic record
Abstract
Reports on a new generation of Internet controls that establish a new normative terrain in which surveillance and censorship are routine.Internet filtering, censorship of Web content, and online surveillance are increasing in scale, scope, and sophistication around the world, in democratic countries as well as in authoritarian states. The first generation of Internet controls consisted largely of building firewalls at key Internet gateways; China's famous “Great Firewall of China” is one of the first national Internet filtering systems. Today the new tools for Internet controls that are emerging go beyond mere denial of information. These new techniques, which aim to normalize (or even legalize) Internet control, include targeted viruses and the strategically timed deployment of distributed denial-of-service (DDoS) attacks, surveillance at key points of the Internet's infrastructure, take-down notices, stringent terms of usage policies, and national information shaping strategies. Access Controlled reports on this new normative terrain. The book, a project from the OpenNet Initiative (ONI), a collaboration of the Citizen Lab at the University of Toronto's Munk Centre for International Studies, Harvard's Berkman Center for Internet and Society, and the SecDev Group, offers six substantial chapters that analyze Internet control in both Western and Eastern Europe and a section of shorter regional reports and country profiles drawn from material gathered by the ONI around the world through a combination of technical interrogation and field research methods.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.310 | 0.126 |
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".