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Record W4200100066 · doi:10.24917/20811861.19.29

Prawda czy fałsz w polityce informacyjnej? Wiarygodność telewizyjnych newsów a opinie polskich widzów w pierwszym kwartale pandemii COVID-19

2021· article· en· W4200100066 on OpenAlexaboutno aff
Evelina Kristanova

Bibliographic record

VenueAnnales Universitatis Paedagogicae Cracoviensis | Studia ad Bibliothecarum Scientiam Pertinentia · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Issues in Poland
Canadian institutionsnot available
Fundersnot available
KeywordsCredibilityContext (archaeology)Coronavirus disease 2019 (COVID-19)Quarter (Canadian coin)Ranking (information retrieval)PsychologyAdvertisingPandemicPoliticsPolitical scienceHistoryComputer scienceBusinessMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

The authors studied the opinions of a randomly selected group of 100 respondents on the subject of policy of Polish and English language television services broadcasted at the very beginning of the COVID-19 pandemic. An anonymous online survey asked respondents about the accuracy and reliability of the information, with the goal of understanding of how much respondents were aware of the influence of all news on their political views and attitude. Media content analysis, critical analysis of literature, as well as comparative and statistical methods were analysed in conjunction with the responses to provide a wider context. The authors were also interested in learning: 1) if people would choose the same TV station, 2) their approval rating of the incumbent president, and finally, 3) whether their preferences would be in line with the official ranking data. The authors concluded that the respondents’ recognition of the credibility of the new sources facilitated the formation of political views, and in the process, their choices.

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)0.002
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0070.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.080
GPT teacher head0.397
Teacher spread0.317 · 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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueAnnales Universitatis Paedagogicae Cracoviensis | Studia ad Bibliothecarum Scientiam PertinentiaSame topicSocial Issues in PolandFrench-language works237,207