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Record W2071606217 · doi:10.5455/aim.2013.21.148-155

The Importance of Proper Citation of References in Biomedical Articles

2013· article· en· W2071606217 on OpenAlexaboutno aff
Izet Mašić

Bibliographic record

VenueActa Informatica Medica · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
Fundersnot available
KeywordsCitationListing (finance)Style (visual arts)Medical journalComputer scienceLibrary scienceOrder (exchange)Writing styleInformation retrievalHistoryLinguistics

Abstract

fetched live from OpenAlex

In scientific circles, the reference is the information that is necessary to the reader in identifying and finding used sources. The basic rule when listing the sources used is that references must be accurate, complete and should be consistently applied. On the other hand, quoting implies verbatim written or verbal repetition of parts of the text or words written by others that can be checked in original. Authors of every new scientific article need to explain how their study or research fits with previous one in the same or similar fields. A typical article in the health sciences refers to approximately 20-30 other articles published in peer reviewed journals, cite once or hundreds times. Citations typically appear in two formats: a) as in-text citations where the sources of information are briefly identified in the text; or b) in the reference list at the end of the publication (book chapter, manuscript, article, etc.) that provides full bibliographic information for each source. Group of publishers met in Vancouver in 1978 and decided to prescribe uniform technical propositions for publication. Adopted in the 1979 by the National Library of Medicine in Bethesda, then the International Committee of Medical Journals Editors (ICMJE), whose review in 1982 entered the official application by 300 international biomedical journals. Authors writing articles for publication in biomedical publications used predominantly citation styles: Vancouver style, Harward style, PubMed style, ICMJE, APA, etc. The paper gives examples of all of these styles of citation to the authors in order to facilitate their applications. Also in this paper is given the review about the problem of plagiarism which becomes more common in the writing of scientific and technical articles in biomedicine.

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)0.133
metaresearch head score (Gemma)0.574
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.701

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1330.574
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0410.058
Science and technology studies0.0070.010
Scholarly communication0.0360.035
Open science0.0040.011
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0080.012

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 head0.025
GPT teacher head0.290
Teacher spread0.265 · 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
DomainReporting
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

Citations79
Published2013
Admission routes1
Has abstractyes

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