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Record W3190419174 · doi:10.1080/17538068.2021.1953934

Public anxiety and distrust due to perceived politicization and media sensationalism during early COVID-19 media messaging

2021· article· en· W3190419174 on OpenAlex
Lauren J. Van Scoy, Bethany Snyder, Erin L. Miller, Olubukola Toyobo, Ashmita Grewel, Giang Ha, Sarah Gillespie, Megha Patel, Jordyn Reilly, Aleksandra Zgierska, Robert P. Lennon

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Communications In Healthcare · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
FundersDepartment of Family and Community Medicine, University of TorontoHuck Institutes of the Life SciencesSocial Science Research Institute, Pennsylvania State University
KeywordsDistrustPandemicSensationalismCredibilityCoronavirus disease 2019 (COVID-19)Source credibilityPublic healthMass mediaAnxietyText messagingHealth communicationDisinformationTrustworthiness2019-20 coronavirus outbreakPsychologyPublic relationsNews mediaPolitical scienceInternet privacySocial mediaAdvertisingMedicineSocial psychologyBusinessVirologyNursingOutbreakComputer science

Abstract

fetched live from OpenAlex

Background Understanding early COVID-19 messaging is essential for improving future public health responses to pandemics. This study applied aspects of both media dependency theory and a source credibility framework to explore how COVID-19 pandemic messaging was perceived by the public within one month of COVID-19 being declared a pandemic.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.131
GPT teacher head0.399
Teacher spread0.268 · 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