Bibliographic record
Abstract
Experts examine censorship, surveillance, and resistance across Asia, from China and India to Malaysia and the Philippines. A daily battle for rights and freedoms in cyberspace is being waged in Asia. At the epicenter of this contest is China—home to the world's largest Internet population and what is perhaps the world's most advanced Internet censorship and surveillance regime in cyberspace. Resistance to China's Internet controls comes from both grassroots activists and corporate giants such as Google. Meanwhile, similar struggles play out across the rest of the region, from India and Singapore to Thailand and Burma, although each national dynamic is unique. Access Contested, the third volume from the OpenNet Initiative (a collaborative partnership of the Citizen Lab at the University of Toronto's Munk School of Global Affairs, the Berkman Center for Internet and Society at Harvard University, and the SecDev Group in Ottawa), examines the interplay of national security, social and ethnic identity, and resistance in Asian cyberspace, offering in-depth accounts of national struggles against Internet controls as well as updated country reports by ONI researchers. The contributors examine such topics as Internet censorship in Thailand, the Malaysian blogosphere, surveillance and censorship around gender and sexuality in Malaysia, Internet governance in China, corporate social responsibility and freedom of expression in South Korea and India, cyber attacks on independent Burmese media, and distributed-denial-of-service attacks and other digital control measures across Asia.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 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".