MétaCan
Menu
Back to cohort
Record W3200417041 · doi:10.2147/amep.s328648

A Survey-Weighted Analytic Hierarchy Process to Quantify Authorship

2021· article· en· W3200417041 on OpenAlexafffund
Edsel Ing

Bibliographic record

VenueAdvances in Medical Education and Practice · 2021
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoJohns Hopkins University
KeywordsAnalytic hierarchy processPairwise comparisonLikert scaleAccountabilityComputer scienceRubricMedical educationPsychologyMedicineStatisticsOperations researchPolitical scienceMathematicsArtificial intelligenceMathematics educationLaw

Abstract

fetched live from OpenAlex

Background: Authorship is a pinnacle activity in academic medicine that often involves collaboration and a mentor–mentee relationship. The International Committee of Medical Journal Editors criteria for authorship (ICMJEc) are intended to prevent abuses of authorship and are used by more than 5500 medical journals. However, the binary ICMJEc have not yet been quantified. Aim: To develop a numeric scoring rubric for the ICMJEc to corroborate the authenticity of authorship claims. Methods: The four ICMJEc were separated into the nine authorship components of conception, design, data acquisition, data analysis, interpretation of data, draft, revision, final approval and accountability. In spring 2021, members of an international association of medical editors rated the importance of each authorship component using an 11-point Likert scale ranging from 0 (no importance) to 10 (most important). The median component scores were used to calibrate the pairwise comparisons in an analytic hierarchy process (AHP). The AHP priority weights were multiplied against a four-level perceived effort/capability grade to calculate an authorship score. Results: Sixty-six decision-making medical editors completed the survey. The components had the median scores/AHP weights: conception 7.5/5.3%; design 8/8.9%; data acquisition 7/3.6%; data analysis 7/3.6%; interpretation of data 8/8.9%; draft 8/8.9%; revision 8/8.9%; final approval 9/20.1%; and accountability 10/31.8%, with Kruskal–Wallis Chi 2 = 65.11, p < 0.001. Conclusion: The editors rated accountability as the most important component of authorship, followed by the final approval of the manuscript; data acquisition had the lowest median importance score for authorship. The scoring rubric ( https://tinyurl.com/eyu86y96 ) transforms the binary tetrad ICMJEc into 9 quantifiable components of authorship, providing a transparent method to objectively assess authorship contributions, determine authorship order and potentially decrease the abuse of authorship. If desired, individual journals can survey their editorial boards and use the AHP method to derive customized weightings for an ICMJEc-based authorship index. Keywords: authorship, ICMJE, academic medicine, ethics, medical editors, analytic hierarchy process, survey

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)0.046
metaresearch head score (Gemma)0.708
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0460.708
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.115
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.475
GPT teacher head0.681
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations9
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueAdvances in Medical Education and PracticeSame topicscientometrics and bibliometrics researchFrench-language works237,207