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Record W4386249603 · doi:10.1109/crv60082.2023.00007

Program Committee: CRV 2023

2023· article· en· W4386249603 on OpenAlexaff
Alexander Ferworn, Amin Mohammad, Soleimani Abyaneh, Carlos Vázquez, David A. Clausi, Elham Daneshmand, Fahim Mannan, Algolux Lotfi, Guillaume-Alexandre Bilodeau, Helge Rhodin, Hughes Perreault, Hui-Lee Ooi, Jack Collier, Drdc Suffield, James J. Clark, James T. Elder, Jean‐François Lalonde, Jochen Lang, John Zelek, Jonathan Kelly, Krista A. Ehinger, Kwang Moo, Liam Paull, Michael S. Brown, Michael Greenspan, Michael Langer, Michaël Clément, Paritosh Parmar, Philippe Giguère, Robert Allison, Robert Bergevin, Rui‐Sheng Wang, Sajad Saeedi, Sandeep Manjanna, Scott McCloskey, Steven L. Waslander, Toews Matthew, Vida Movahedi, Yang Wang, Yanshu Zhang, Simon Fraser

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of CalgaryUniversité du Québec en OutaouaisUniversity of AlbertaMcGill UniversitySimon Fraser UniversityUniversity of OttawaUniversité de MontréalUniversité LavalQueen's UniversityPolytechnique MontréalUniversity of WaterlooYork UniversityUniversity of British ColumbiaSeneca PolytechnicUniversity of TorontoÉcole de Technologie SupérieureToronto Metropolitan University
Fundersnot available
KeywordsComputer 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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.461
Threshold uncertainty score0.657

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0060.001
Open science0.0050.003
Research integrity0.0100.004
Insufficient payload (model declined to judge)0.5390.368

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 head0.049
GPT teacher head0.268
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

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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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