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Record W2142538319 · doi:10.1525/jer.2013.8.5.75

A Bibliometric Analysis of an International Research Ethics Trainee Program

2013· article· en· W2142538319 on OpenAlexafffund
Jonathan Fix, Jere D. Odell, Barbara Sina, Eric M. Meslin, Ken Goodman, Ross Upshur

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2013
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsBioethicsBibliometricsCurriculumCitation analysisCitationIndex (typography)Library scienceMedical educationPsychologySociologySocial sciencePolitical scienceMedicineComputer sciencePedagogyLaw

Abstract

fetched live from OpenAlex

We used bibliometric analysis to evaluate the citations associated with publications by trainees in the Fogarty International Center's International Research Ethics Education and Curriculum Development program. Papers published between 2004 and 2008 were identified for analysis. The outcome measures were total citations, h-index, and i-10. A total of 328 manuscripts were identified, with a yearly average of 66 publications and 363 citations. The median number of citations per paper is 3 (IQR Q1-Q3:6). 12.6% (n = 53) of papers were cited over 10 times and the h-index is 22, indicating that 22 papers had been cited at least 22 times. The data indicate that trainees have been productive and contributed to the scholarly literature. Future studies to benchmark this performance with other bioethics education programs are required to make interpretation of citation analysis more meaningful.

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)0.427
metaresearch head score (Gemma)0.681
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4270.681
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.2850.281
Science and technology studies0.0020.011
Scholarly communication0.0010.001
Open science0.0060.002
Research integrity0.0040.134
Insufficient payload (model declined to judge)0.0040.000

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 head0.962
GPT teacher head0.818
Teacher spread0.145 · 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 designObservational
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

Citations31
Published2013
Admission routes2
Has abstractyes

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