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Record W2998095279 · doi:10.1002/asi.24330

The citation advantage of promoted articles in a cross‐publisher distribution platform: A 12‐month randomized controlled trial

2019· article· en· W2998095279 on OpenAlexafffund
Paul Kudlow, Devin Bissky Dziadyk, Alan Rutledge, Aviv Shachak, Günther Eysenbach

Bibliographic record

VenueJournal of the Association for Information Science and Technology · 2019
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsMaRSToronto Rehabilitation InstituteUniversity of Toronto
FundersOntario Centres of Excellence
KeywordsRandomized controlled trialMedicineConfidence intervalCitationInternal medicineComputer scienceLibrary science

Abstract

fetched live from OpenAlex

Abstract There is currently a paucity of evidence‐based strategies that have been shown to increase citations of peer‐reviewed articles following their publication. We conducted a 12‐month randomized controlled trial to examine whether the promotion of article links in an online cross‐publisher distribution platform (TrendMD) affects citations. In all, 3,200 articles published in 64 peer‐reviewed journals across eight subject areas were block randomized at the subject level to either the TrendMD group ( n = 1,600) or the control group ( n = 1,600) of the study. Our primary outcome compares the mean citations of articles randomized to TrendMD versus control after 12 months. Articles randomized to TrendMD showed a 50% increase in mean citations relative to control at 12 months. The difference in mean citations at 12 months for articles randomized to TrendMD versus control was 5.06, 95% confidence interval [2.87, 7.25], was statistically significant ( p < .001) and found in three of eight subject areas. At 6 months following publication, articles randomized to TrendMD showed a smaller, yet statistically significant ( p = .005), 21% increase in mean citations, relative to control. To our knowledge, this is the first randomized controlled trial to demonstrate how an intervention can be used to increase citations of peer‐reviewed articles after they have been published.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometricsMetaresearch
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized triallow
gptMetaresearch
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized trialhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.098
metaresearch head score (Gemma)0.290
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0980.290
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.042
Science and technology studies0.0000.000
Scholarly communication0.0020.005
Open science0.0010.000
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 head0.095
GPT teacher head0.450
Teacher spread0.355 · 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

Labeled directly by 2 models reading the full record.

BibliometricsMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designRandomized trial
DomainEvaluation
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

Citations11
Published2019
Admission routes2
Has abstractyes

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