"Positive Energy"
Bibliographic record
Abstract
The COVID-19 outbreak has resulted in a worldwide public health crisis. In such times of crisis, access to relevant and accurate information is critical. For many people in China, domestic social media platforms such as WeChat and Weibo have become dominant sources of COVID-19-related information and news. People have to evaluate the trustworthiness of COVID-19-related information and make sharing decisions using platforms that have to contend with government censorship policies, astroturfers, and other government interventions. We interviewed 33 Chinese WeChat users to understand how individuals were seeking COVID-19-related information and how they identified and evaluated specific COVID-19-related misinformation. This work exposes how COVID-19-related content with "positive energy" was prevalent on social media in China. A significant number of interviewees exhibited a willingness to prioritize information valence over veracity when evaluating and sharing content with others. Further, the work revealed how Chinese citizens' understanding of information ecosystems played an important role in their attitudes towards censorship and official media, and also influenced their evaluation of domestic and international information during a global crisis.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".