MétaCan
Menu
Back to cohort
Record W4210652258 · doi:10.5539/gjhs.v14n2p63

Myths about Coronavirus: A Research Defense

2022· article· en· W4210652258 on OpenAlexvenueno aff
Md. Rahimullah Miah, Md Mehedi Hasan, Mohammad Abdul Hannan, Jorin Tasnim Parisa, Mohammad Jasim Uddin, Mohammad Basir Uddin, Ashiqur Rahman, Sadeed Hossain, Md. Amir Sharif, Foujia Akhtar, Mir Abu Saleh Shamsuddin, Md. Sher-E-Alam, Mohammad Shamsul Alam, Fuad Abdullah, Md. Shoaibur Rahman, Mohammad Belal Uddin, Chowdhury Shadman Shahriar, Alexander Kiew Sayok, Motia Begum, Md Mokbul Hossain, Md Shahariar Khan, Guljar Ahmed, Syeda Umme Fahmida Malik, Md Kamrul Husain Azad, Alamgir Adil Samdany, Mohammad Abdul Ghani, Md. Sabbir Hossain, Mosammat Suchana Nazrin, Md Roshaid Ahmed Tamim, Monique Sélim, Mohammad Taimur Hossain Talukdar, Farhana Tasnim Chowdhury, Towhid-ur- Rashid, Abu Yousuf Md Nazim, Muhammad Mahbubur Rashid, Shahriar Hussain Chowdhury

Bibliographic record

VenueGlobal Journal of Health Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
FundersUniversiti Malaysia Sarawak
KeywordsPandemicCoronavirusCoronavirus disease 2019 (COVID-19)Internet privacyPolitical scienceMedicineDiseaseComputer scienceInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

The discovery of coronavirus is a state-of-the-art scientific study, so there is no doubt in the whole world that it is an innovation. But cybercriminals are working to create suspicion so that people are confused and afraid of the coronavirus. Cybercriminals are committing crimes from the global cloud network. They are carrying out extrajudicial killings around the world by abusing wireless sensor technology based on the victim’s active open eye, voice, adjacent sensor device and a specific GPS location, while spreading lies about the coronavirus pandemic in the media. New variants of the coronavirus are being created by cybercriminals and over time cyber killer groups are spreading to target-based regions of the universe, putting the world in a serious crisis threatening everyone’s survival. Surveys show that the death toll is rising and no one can say when the coronavirus will end and everyone will regain their right to life. The study indicates that lawmakers, health workers and technologists are blindfolded and foolishly looking for vaccines as a way to escape the coronavirus in the open air while others are dying helplessly. For coronavirus pandemic management and disease-free living for all, their sensor technological knowledge is essential, but such knowledge is insufficient. This is because cybercriminals have hypnotized each of their brains to their specific GPS location, depriving them of the right to decide for exposure in the media. The results of a unique study - coronavirus survival and its origin - published research is very difficult to reach relevant legislators and stakeholders. Research published on the publisher’s website has been bounced by cybercriminals for political malpractice and lack of proper sensor security. The study illustrates the interrelationship among the political agenda, mysterious lockdowns and digital assassination through global wireless clouding. Today or the day after tomorrow, the importance of this research should be made known to all, otherwise no one will be saved from the digital death of cyber criminals. Lying about coronavirus is nothing more than propaganda of GPS sensor abusers. To avoid this propaganda, everyone in the world must be aware and the legal system of each country must be properly implemented to suppress cyber criminals. This advanced study is an absolute witness to the building of a peaceful world for all generations, which encourages all to be vigilant and create a conscious circle to deal with this pandemic while maintaining equality and neutrality.

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)0.019
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0070.040
Scholarly communication0.0110.035
Open science0.0030.007
Research integrity0.0150.039
Insufficient payload (model declined to judge)0.0090.004

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 head0.184
GPT teacher head0.516
Teacher spread0.333 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicMisinformation and Its ImpactsFrench-language works237,207