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Record W4249959585 · doi:10.31219/osf.io/6tcdn

The Causes and Consequences of COVID-19 Misperceptions: Understanding the Role of News and Social Media

2020· preprint· en· W4249959585 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of TorontoMcGill University
Fundersnot available
KeywordsMisinformationSocial distanceSocial mediaCoronavirus disease 2019 (COVID-19)PandemicPsychologySocial psychologyNewspaper2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Consumption (sociology)Political scienceAdvertisingSociologyMedicineBusinessVirologyOutbreak

Abstract

fetched live from OpenAlex

We investigate the relationship between media consumption, misinformation, and important attitudes and behaviours during the COVID-19 pandemic in Canada. We find that comparatively more misinformation circulates on social media platforms, while traditional news media tend to reinforce public health recommendations like social distancing. We find that exposure to social media is associated with misperceptions about COVID-19 while the inverse is true for news media. These misperceptions are in turn associated with lower compliance with social distancing measures. We thus draw a link from misinformation on social media to behaviours and attitudes that potentially magnify the scale and lethality of COVID-19.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.187
GPT teacher head0.379
Teacher spread0.192 · 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

Quick stats

Citations113
Published2020
Admission routes2
Has abstractyes

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