Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it