Assessing YouTube science news’ credibility: The impact of web-search on the role of video, source, and user attributes
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
= 707) is the first to examine the role of video, source and user attributes in credibility assessment of online science news videos, and the impact of web-search on this role. We created a science news YouTube video in 12 versions (3 × 2 × 2 for source, quality and popularity). Each participant was randomly assigned to one version and was asked to rate the credibility of the source and the scientific information presented in the video. We found that perceived credibility is positively associated with perceived quality, as well as users' YouTube experience. For those participants who did not conduct an online search during the assessment task, there was a positive association between the presenter's perceived credibility and the video's perceived credibility as well as its popularity; however, such associations were not present for participants who did conduct an online search.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.001 | 0.003 |
| 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 it