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Beauty is Truth, Truth Beauty: Students’ Assessment of Credibility in Online Materials

2023· article· en· W4390044055 on OpenAlexaffvenueabout
Ralf St. Clair, Maryam Shirdel Pour, James Nahachewsky

Bibliographic record

VenueInternational journal of e-learning & distance education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCredibilityPsychologyLigneBeautySocial psychologyHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

This study discusses the findings of a survey designed to capture students’ allocations of credibility to online materials resembling social media posts. The survey respondents were 1,019 undergraduate students at a medium-sized Canadian university. The students came from a range of programs and years of study in those programs. The survey instrument presented varying stimuli to students to see how their scores varied, and then asked students to explain their scoring. A number of significant dynamics emerged, such as the students’ tendency to give lower credibility scores to poorly presented information, even if the information was factual, and to explain information by referring to previous knowledge. These dynamics varied little by area or year of study, which suggests that presentation should be recognized as a powerful heuristic in online credibility assessment. Keywords: credibility, social media, undergraduate, survey research La beauté est la vérité , la vérité est la beauté : L'évaluation par les étudiants de la crédibilité des documents en ligne Résumé : Cette étude présente les résultats d'une enquête visant à déterminer la crédibilité que les étudiants accordent aux documents en ligne ressemblant à des messages de médias sociaux. Les répondants à l'enquête étaient 1 019 étudiants de premier cycle d'une université canadienne de taille moyenne. Les étudiants provenaient d'un éventail de programmes et de différentes années d'études dans ces programmes. L'instrument d'enquête présentait différents stimuli aux étudiants afin de voir comment leurs scores variaient, et demandait ensuite aux étudiants d'expliquer leur notation. Un certain nombre de dynamiques significatives sont apparues, telles que la tendance des étudiants à accorder des scores de crédibilité plus faibles aux informations mal présentées, même lorsqu'elles sont factuelles, et à expliquer les informations en se référant à des connaissances antérieures. Ces dynamiques varient peu en fonction du domaine ou de l'année d'étude, ce qui suggère que la présentation devrait être reconnue comme une heuristique puissante dans l'évaluation de la crédibilité en ligne. Mots-clés : crédibilité, médias sociaux, premier cycle universitaire, enquête de recherche

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.450
Teacher spread0.417 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes3
Has abstractyes

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