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Record W4405851911 · doi:10.3346/jkms.2025.40.e76

Insights Into <i>JKMS</i> Submissions and Medical Journal Publications in Korea

2024· article· en· W4405851911 on OpenAlex
Jaehun Jung, Yumi Jang, Munkhzul Radnaabaatar, Dae Sun Jo, Jong‐Min Kim, Jin‐Hong Yoo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Korean Medical Science · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsMEDLINEMedicineData scienceLibrary scienceComputer sciencePolitical science

Abstract

fetched live from OpenAlex

We analyzed the publication and submission statuses of Korean medical journals from 2010 to 2024, amidst challenges impacting researchers.Data from 58 domestic journals identified through the 2023 JCR database were used to assess publication status, while data from the Journal of Korean Medical Science (JKMS) were utilized to examine submission status.The proportion of published original articles by domestic authors decreased by 3% in 2024 compared to 2023.Submissions to JKMS also decreased overall, except for slight increases in May and October 2024.In contrast, international submissions to JKMS showed consistent growth, surpassing the 15-year average, reflecting growing global interest.Addressing issues, including medical school admission policies and the lingering effects of coronavirus disease 2019, is vital to ensure a sustainable and thriving medical research environment in Korea.

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.

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)0.039
metaresearch head score (Gemma)0.137
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.137
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0060.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.423
Teacher spread0.364 · 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