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Record W2951578548 · doi:10.1371/journal.pone.0184601

The rise of the middle author: Investigating collaboration and division of labor in biomedical research using partial alphabetical authorship

2017· article· en· W2951578548 on OpenAlexafffund
Philippe Mongeon, Elise Smith, Bruno Joyal, Vincent Larivière

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité du Québec à MontréalMcGill UniversityUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOperationalizationListing (finance)Order (exchange)Period (music)PsychologyComputer scienceEpistemologyBusiness

Abstract

fetched live from OpenAlex

Contemporary biomedical research is performed by increasingly large teams. Consequently, an increasingly large number of individuals are being listed as authors in the bylines, which complicates the proper attribution of credit and responsibility to individual authors. Typically, more importance is given to the first and last authors, while it is assumed that the others (the middle authors) have made smaller contributions. However, this may not properly reflect the actual division of labor because some authors other than the first and last may have made major contributions. In practice, research teams may differentiate the main contributors from the rest by using partial alphabetical authorship (i.e., by listing middle authors alphabetically, while maintaining a contribution-based order for more substantial contributions). In this paper, we use partial alphabetical authorship to divide the authors of all biomedical articles in the Web of Science published over the 1980-2015 period in three groups: primary authors, middle authors, and supervisory authors. We operationalize the concept of middle author as those who are listed in alphabetical order in the middle of an authors' list. Primary and supervisory authors are those listed before and after the alphabetical sequence, respectively. We show that alphabetical ordering of middle authors is frequent in biomedical research, and that the prevalence of this practice is positively correlated with the number of authors in the bylines. We also find that, for articles with 7 or more authors, the average proportion of primary, middle and supervisory authors is independent of the team size, more than half of the authors being middle authors. This suggests that growth in authors lists are not due to an increase in secondary contributions (or middle authors) but, rather, in equivalent increases of all types of roles and contributions (including many primary authors and many supervisory authors). Nevertheless, we show that the relative contribution of alphabetically ordered middle authors to the overall production of knowledge in the biomedical field has greatly increased over the last 35 years.

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)0.052
metaresearch head score (Gemma)0.261
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0520.261
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.042
Science and technology studies0.0010.003
Scholarly communication0.0010.000
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.870
GPT teacher head0.601
Teacher spread0.268 · 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

Citations64
Published2017
Admission routes2
Has abstractyes

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