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Record W3008656998 · doi:10.1177/0963662520905466

Assessing YouTube science news’ credibility: The impact of web-search on the role of video, source, and user attributes

2020· article· en· W3008656998 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePublic Understanding of Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCredibilityPopularitySource credibilityQuality (philosophy)PsychologyComputer scienceInternet privacyApplied psychologyWorld Wide WebSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

= 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0020.014
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.227
GPT teacher head0.382
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it