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Record W3133191603 · doi:10.21203/rs.3.rs-237881/v1

Bibliometric study of ‘overviews of systematic reviews’: evaluation of prevalence, citation and impact factor

2021· preprint· en· W3133191603 on OpenAlexaff
Carole Lunny, Trish Neelakant, Alyssa Chen, Gavindeep Shinger, Adrienne Stevens, Sara Tasnim, Shadi Sadeghipouya, Stephen Adams, Yi Wen Zheng, Lester Lin, Pei‐Hsuan Yang, Manpreet Dosanjh, Peter Ngsee, Ursula Ellis, Beverley Shea, Emma K. Reid, James M Wright

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsNova Scotia Health AuthorityUniversity of OttawaCochraneUniversity of British Columbia
Fundersnot available
KeywordsCitationImpact factorSystematic reviewMedicineComputer scienceMEDLINELibrary sciencePolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.237
metaresearch head score (Gemma)0.721
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.941

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2370.721
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0100.018
Bibliometrics0.1220.127
Science and technology studies0.0020.003
Scholarly communication0.0080.009
Open science0.0030.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.950
GPT teacher head0.701
Teacher spread0.249 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designMeta-analysis
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

Citations5
Published2021
Admission routes1
Has abstractno

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