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Record W4404905567 · doi:10.32920/27940890.v1

Using Google Scholar to track the scholarly output of research groups

2024· preprint· en· W4404905567 on OpenAlexaff
Brent Thoma, Teresa M. Chan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsMcMaster UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsTrack (disk drive)Computer scienceWorld Wide WebData scienceInformation retrievalOperating system

Abstract

fetched live from OpenAlex

<p>Introduction: It is often necessary to demonstrate the impact of a research program over time both within and beyond institutions. However, it is difficult to accurately track the publications of research groups over time without significant effort. A simple, scalable, and economical way to track publications from research groups and their metrics would address this challenge.</p> <p>Methods: Google Scholar automatically tracks the scholarly output and citation counts of individual researchers. We created Google Scholar profiles to track the scholarly productivity of five research groups: an institutional educational research program, a division of emergency medicine, a department of emergency medicine, a national educational scholarship working group, and an international organization dedicated to online education. We added the publications of each group member to their respective group Google Scholar profile and a junior faculty member monitored the citations that were suggested.</p> <p>Results: Google Scholar tracked a diverse collection of five research groups over 6-36 months. In addition to having different organizational structures and purposes, the groups varied in size, consisting of 8-60 researchers, and prolificacy, with group citation counts between 1006-58,380 and group h‑indexes ranging from 19-101.</p> <p>Discussion: We anticipate that as this innovation becomes better known it will increasingly be adopted by traditional and non-traditional research groups to easily track their productivity and impact. Additional initiatives will be needed to standardize reporting guidelines within and between institutions.</p>

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
gemmaBibliometrics
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptBibliometrics
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
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)0.214
metaresearch head score (Gemma)0.165
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics, Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2140.165
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.1610.350
Science and technology studies0.0010.001
Scholarly communication0.0340.001
Open science0.0140.030
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0010.004

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 head0.903
GPT teacher head0.685
Teacher spread0.218 · 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.

Bibliometrics

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

Study designNot applicable · Other design
Domainnot available
GenreMethods

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

Citations0
Published2024
Admission routes1
Has abstractyes

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