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Record W4383334972 · doi:10.2139/ssrn.4487105

A Scoping Review to Identify Candidate Quality Indicators of Tools that Support the Practice of Knowledge Translation

2023· review· en· W4383334972 on OpenAlexaff
Aunima R. Bhuiya, Justin Sutherland, Rhonda Boateng, Julie Makarski, Laure Perrier, Sarah Munce, Iveta Lewis, Ian D. Graham, Jayna Holroyd‐Leduc, Sharon E. Straus, Lisa Strifler, Cynthia Lokker, Linda Li, Fok‐Han Leung, Maureen Dobbins, Janet E. Squires, Valeria E. Rac, Christine Fahim, Monika Kastner

Bibliographic record

VenueSSRN Electronic Journal · 2023
Typereview
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of OttawaUniversity of TorontoUniversity Health NetworkMcMaster University
Fundersnot available
KeywordsKnowledge translationQuality (philosophy)Translation (biology)Knowledge managementComputer scienceData scienceProcess managementBusinessEpistemologyBiology

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)0.103
metaresearch head score (Gemma)0.287
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score0.546

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1030.287
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0100.010
Bibliometrics0.0500.039
Science and technology studies0.0030.003
Scholarly communication0.0100.010
Open science0.0050.006
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.107
GPT teacher head0.472
Teacher spread0.365 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainMethods
GenreReview

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
Published2023
Admission routes1
Has abstractno

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