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Record W3164551198 · doi:10.2196/22271

Cyberspace and Libel: A Dangerous Balance for Physicians

2021· article· en· W3164551198 on OpenAlexvenueno aff
Varsha Chiruvella, Achuta Kumar Guddati

Bibliographic record

VenueInteractive Journal of Medical Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaCyberspaceContext (archaeology)MisinformationDutyFreedom of the pressPolitical scienceBattleReputationPoliticsPublic sphereLawThe InternetPublic relationsInternet privacyHistory

Abstract

fetched live from OpenAlex

Freedom of speech and expression is one of the core tenets of modern societies. It was deemed to be so fundamentally essential to early American life that it was inscribed as the First Amendment of the United States Constitution. Over the past century, the rise of modern life also marked the rise of the digital era and age of social media. Freedom of speech thus transitioned from print to electronic media. Access to such content is almost instantaneous and available to a vast audience. From social media to online rating websites, online defamation may cause irreparable damage to a physician's reputation and practice. It is especially relevant in these times of political turbulence where the battle to separate facts from misinformation has started a debate about the responsibility of social media. The historical context of libel and its applicability in the age of increasing online presence is important for physicians since they are also bound by duty to protect the privacy of their patients. The use of public rating sites and social media will continue to be important for physicians, as online presence and incidents of defamation impact the practice of medicine.

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 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)0.003
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.338
GPT teacher head0.593
Teacher spread0.255 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations2
Published2021
Admission routes1
Has abstractyes

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