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Record W4211156566 · doi:10.1109/trpms.2022.3145116

ADMINISTRATIVE COMMITTEE

2022· article· en· W4211156566 on OpenAlexfundno aff

Bibliographic record

VenueIEEE Transactions on Radiation and Plasma Medical Sciences · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicEducation, Management, Technology, Human Resources
Canadian institutionsnot available
FundersUniversity of California, DavisStony Brook UniversityOld Dominion UniversityUniversità di BolognaVrije Universiteit BrusselCentre National de la Recherche ScientifiqueMcGill UniversityUniversity of South AustraliaInstitut National de la Santé et de la Recherche MédicaleUniversity College LondonUniversity of PatrasKwangwoon UniversityUniversity of Texas MD Anderson Cancer CenterUniverzita Komenského v BratislaveUniversità di PisaYale UniversitySeoul National UniversityUniversitat de ValènciaHarvard UniversityWest Virginia UniversityUniversity of PennsylvaniaGeorge Washington UniversityKorea Advanced Institute of Science and TechnologyShanghai Jiao Tong UniversityUniversity of WashingtonRensselaer Polytechnic Institute
KeywordsPolitical 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)0.030
metaresearch head score (Gemma)0.100
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 score0.519
Threshold uncertainty score0.741

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.100
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.004
Science and technology studies0.0060.002
Scholarly communication0.0110.003
Open science0.0060.004
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.4810.426

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 head0.153
GPT teacher head0.408
Teacher spread0.255 · 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".

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Citations0
Published2022
Admission routes1
Has abstractno

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