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Record W4233946130 · doi:10.2196/26225

Correction: Documenting Social Media Engagement as Scholarship: A New Model for Assessing Academic Accomplishment for the Health Professions

2020· article· en· W4233946130 on OpenAlexafffund
Kimberly Acquaviva, Josh Mugele, Natasha Abadilla, Tyler Adamson, Samantha Bernstein, Rakhee K Bhayani, Annina Elisabeth Büchi, Darcy Burbage, Christopher L. Carroll, Samantha Davis, Natasha Dhawan, Alice Eaton, Kim Ēnglish, Jennifer T. Grier, Mary K. Gurney, Emily S Hahn, Heather Haq, Brendan Huang, Shikha Jain, Jin Jun, Wesley T. Kerr, Timothy Keyes, Amelia R Kirby, Marion Leary, Mollie Marr, Ajay Major, Jason V Meisel, Erika Petersen, Barak Raguan, Allison Rhodes, Deborah D. Rupert, Nadia A. Sam‐Agudu, Naledi Saul, Jarna R Shah, Lisa Kennedy Sheldon, Christian T. Sinclair, Kerry Spencer, Natalie Strand, Carl G. Streed, Avery M Trudell

Bibliographic record

VenueJournal of Medical Internet Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsFleming College
FundersReseau canadien de recherche respiratoire
KeywordsScholarshipSocial mediaPsychologyHealth professionsData scienceComputer scienceWorld Wide WebMedical educationSociologyApplied psychologyHealth careMedicinePolitical science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.010
metaresearch head score (Gemma)0.231
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.231
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.005
Science and technology studies0.0080.008
Scholarly communication0.0090.005
Open science0.0070.005
Research integrity0.0230.029
Insufficient payload (model declined to judge)0.0300.028

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 head0.658
GPT teacher head0.643
Teacher spread0.015 · 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.

Study designTheoretical or conceptual
DomainEvaluation
GenreMethods

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
Published2020
Admission routes2
Has abstractyes

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