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Record W4367322153 · doi:10.3138/jsp-2022-0043

Reflection on ResearchGate’s Terminating ResearchGate Score, and Interest Score, as Social Media Altmetrics and Academic Evaluation Tools

2023· article· en· W4367322153 on OpenAlexvenueno aff
Jaime A. Teixeira da Silva, Yuki Yamada

Bibliographic record

VenueJournal of Scholarly Publishing · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
Fundersnot available
KeywordsAltmetricsMetric (unit)ReputationComputer scienceSocial mediaF1 scoreWeightingRanking (information retrieval)RemunerationReflection (computer programming)SociologyPsychologyInformation retrievalData scienceWorld Wide WebBusinessMarketingArtificial intelligenceSocial scienceMedicineFinance

Abstract

fetched live from OpenAlex

ResearchGate (RG) is a popular academic social media networking platform for scientists, researchers, or academics (SRAs). RG automatically provides a metric, the RG Score, to each RG account holder that serves as a measure of that SRA’s “academic” worth, productivity, and interaction with other SRAs. In 2017, this metric was described by RG as “the RG Score takes all your research and turns it into a source of reputation,” indicating that “it is calculated based on the research in your profile and how other researchers interact with your content.” However, the precise manner in which the RG Score is calculated was never made public because it is a proprietary algorithm, and requests to RG to disclose details of the equations used to calculate it were not met. Not unsurprisingly, RG announced that it would be phasing out the RG Score in June 2022. This article examines what is known in the literature about the RG Score, which may be perceived as a skewed metric because it may add excessive weighting to select aspects, such as questions and answers, rather than to the published literature of an SRA. The RG Interest Score is also critiqued. An author-based metric such as the RG Score that reflects a realistic balance between the most important academic factors while downplaying fairly redundant aspects such as the volume of answers might benefit SRAs. As for any metric, the RG Score should not be used in isolation, be gamed, or used as the basis of any financial remuneration schemes.

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
gemmaMetaresearchBibliometrics
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptBibliometricsMetaresearch
Domain: Evaluation · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
models agreeAgreement 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)0.057
metaresearch head score (Gemma)0.443
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.443
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0120.035
Open science0.0010.000
Research integrity0.0010.004
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 head0.710
GPT teacher head0.554
Teacher spread0.156 · 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.

Study designTheoretical or conceptual
DomainEvaluation
GenreEmpirical · Commentary

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Scholarly PublishingSame topicSocial Media in Health EducationCategoryMetaresearchFrench-language works237,207