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Record W2148661872 · doi:10.3325/cmj.2008.1.18

Short History of Just Mentorship and Support

2008· article· en· W2148661872 on OpenAlexaff
Vladimir J. Šimunović, Milivoj Petković, Sebastianó Miscia, Mirko Petrović, Robert Stallaerts, Werner Busselmaier, Michael Hebgen, Axel Horsch, Slobodanka Horsch, Mojca Kržan, Igor Švab, Samo Ribarič, Danica Železnik, Joanna Santa Barbara, Dalibor Arapović, Tomica Božić, Goran Đuzel, Frano Ljubić, Maja Ostojić, Siniša Skočibušić, Nada Spasojević, Amra Zalihić, Radivoje Radić, Bakir Mehić, Emina Nakaš-Ićindić, Darko Kordić, Damir Sapunar, Snježana Tomić, Farid Ljuca, Nurka Pranjić, Hajrija Selešković, Husref Tahirović, Nijaz Tihić, Zeljko J. Bosnjak, Stjepan Gamulin, Ilija Kuzman, Zdravko Mandić, Selma Kamberović, Marija Hiljadnikova, Haris Tanović

Bibliographic record

VenueCroatian Medical Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMcMaster University
FundersNational Institute of General Medical Sciences
KeywordsPublishingPresentation (obstetrics)PityMentorshipFoundation (evidence)Statement (logic)Scientific publishingPublic relationsMedical educationPsychologyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Since its foundation in 1992, the Croatian Medical Journal (CMJ) has followed the strict standards of quality in the scientific publishing. However, the Journal has been aware that its specific position demands more than just following the already established rules. From the very beginning, the Journal declared an "author-helpful policy," stating that "journal editors should have a major role in training authors in science communication, especially in smaller and developing scientific communities. Journal authors usually send scientifically acceptable but poorly prepared articles and it is a pity to lose valid data because of their poor presentation." (1,2).

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)0.013
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.038
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0090.007
Open science0.0020.012
Research integrity0.0050.015
Insufficient payload (model declined to judge)0.0380.014

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 head0.217
GPT teacher head0.415
Teacher spread0.198 · 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 designNot applicable
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

Citations4
Published2008
Admission routes1
Has abstractyes

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