Exploring factors predicting scientists’ intentions to participate in crisis communication during the COVID-19 pandemic
Bibliographic record
Abstract
This study adopted a questionnaire design. We commissioned two leading WeChat public accounts (social media-based media outlets) that primarily target scientists – iScientist (WeChat ID: IamaScientist) and Fanpu (the Chinese name of the public account, which can be literally translated to “Returning to theoretical purity”, WeChat ID: fanpu2019) – to distribute the online questionnaire from February 19, 2020, when China was still suffering COVID-19 and the pandemic was beginning to spread in other places around the world. Public accounts on WeChat are social media channels that are widely adopted across China. With 1.112 billion monthly active users in the first quarter of 2019, WeChat is China's largest social media platform(Statista, 2020). Both iScientist and Fanpu had more than 100,000 subscribers on WeChat, mainly scientists, engineers, and doctoral students. The advertisement for the survey and the link to the questionnaire were posted in Chinese by the two publications and distributed in iScientist’s weekly E-newsletters among its registered readers (Fanpu didn’t provide E-newsletters), with the title highlighting that the survey focused on scientists’ communication behaviors. The questionnaire was distributed through both Qualtrics.com, an international survey platform (used initially out of concern about potential censorship in China at the onset of the pandemic), and Wjx.cn, a leading Chinese survey site. Data from the two platforms were combined into a single sample. The survey was anonymous, and the introduction clearly stated that participation was voluntary, that completion of the survey implied consent, and that respondents could withdraw at any time. Respondents who completed the questionnaire were offered a complimentary electronic book on science communication skills. The survey remained open for four weeks and was closed after sufficient data had been collected (856 completed questionnaires). We first excluded respondents who spent too little time completing the survey (≤300 seconds), and obtained a total of 802 valid questionnaires. Although the survey invitation specified that we were recruiting scientists—referring, in the Chinese context, primarily to natural science researchers—some ineligible participants still responded. We therefore screened out humanities and social science researchers, as well as non-research professionals (e.g., executives at research institutions), who numbered 44, accounting for about 5.5% of valid respondents, because their perspectives on emergency communication may differ substantially from those of natural scientists. The final dataset included 758 valid responses, with over 300 collected via Qualtrics.com and more than 450 via Wjx.cn. The questionnaire is detailed in the Supplementary material.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".