PRIVACY AND POWER AROUND BEIJING 2022 OLYMPICS: LEGAL AND POLITICAL PERSPECTIVES
Bibliographic record
Abstract
The Olympic Winter Games Beijing 2022 launched a smartphone application, MY2022, to monitor the health status of participants to control the spread of the virus. However, Citizen Lab, a Canadian research institute, found that the application has weak or unencrypted encryption in the data storage and transmission process, leading to users' privacy being exposed to potential leakage risk. In addition, the study found that the app's instant messaging feature contained a list of sensitive words that had not been activated. Although Citizen Lab's report stated that these security vulnerabilities might be unintentional failures by developers rather than an intentional arrangement by the Chinese government, the criticisms were still widely cited by international media, bringing pressure for the Chinese Olympic Committee and the Chinese authorities. Despite that International Olympic Committee and the Chinese Olympic Committee declared to have fixed the bugs after the release of the research report, much beyond the technical failures awaits Beijing to learn.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".