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Record W4296175712 · doi:10.28918/iqtida.v2i01.5124

Penyebaran Berita Hoaks Seputar Fatwa Haram Vaksinasi Covid-19

2022· article· en· W4296175712 on OpenAlexaboutno aff
Dikhorir Afnan

Bibliographic record

VenueIQTIDA Journal of Da wah and Communication · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsHoaxDisinformationCoronavirus disease 2019 (COVID-19)CommitPolitical scienceQuarter (Canadian coin)Social mediaAdvertisingInternet privacyMedicineHistoryComputer scienceLawBusiness

Abstract

fetched live from OpenAlex

The reality today is that there are still many Indonesians who intentionally or unintentionally commit violations in the form of spreading false news or hoaxes. In the first quarter of the start of the Covid-19 vaccination program, the Ministry of Communication and Information noted that there were 117 false messages related to the Covid-19 vaccine spread among 634 uploads on various social media platforms. The purpose of this study is to describe any hoax news content that has ever gone viral in the community related to Covid-19 vaccination during the January 2021 period. The method used in this study is qualitative descriptive of certain phenomena based on data obtained in detail according to the problems specified. in research. As for the results of this study, there were 42 hoax news about Covid-19 vaccination during the January 2021 period. Of that number, the most spread of hoax news and disinformation occurred on January 18, 2021, namely six news.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.039
GPT teacher head0.339
Teacher spread0.300 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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