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Record W4388195090 · doi:10.1101/2023.10.25.23297543

The TARCiS statement: Guidance on terminology, application, and reporting of citation searching

2023· preprint· en· W4388195090 on OpenAlexfundno aff
Julian Hirt, Thomas Nordhausen, Thomas Fuerst, Hannah Ewald

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignKarl Landsteiner Privatuniversität für GesundheitswissenschaftenCancer Research UKDalhousie UniversityEuropean CommissionInstitute of Museum and Library ServicesNorthwestern UniversityNational Institute for Health and Care ExcellenceAlfred P. Sloan FoundationUniversität BaselNational Institutes of HealthNational Science Foundation
KeywordsTerminologyCitationDelphi methodSystematic reviewCornerstoneComputer scienceStatement (logic)WorkflowInformation retrievalContext (archaeology)Data scienceScientific literatureBibliometricsDelphiMEDLINEData miningWorld Wide WebPolitical scienceArtificial intelligenceDatabaseGeography

Abstract

fetched live from OpenAlex

ABSTRACT Evidence syntheses adhering to systematic literature searching techniques are a cornerstone of evidence-based health care. Beyond term-based searching in electronic databases, citation searching is a prevalent search technique to identify relevant sources of evidence. However, for decades, citation searching methodology and terminology has not been standardized. We performed an evidence-guided four-round Delphi consensus study with 27 international methodological experts in order to develop the Terminology, Application, and Reporting of Citation Searching (TARCiS) statement. TARCiS comprises ten specific recommendations on when and how to conduct and report citation searching in the context of systematic literature searches and four research priorities. We encourage systematic reviewers and information specialists to incorporate TARCiS into their standardized workflows.

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)0.015
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.304
GPT teacher head0.514
Teacher spread0.211 · 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; a candidate call from one teacher head, not a consensus.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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