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Record W4327499178 · doi:10.1007/978-3-031-28241-6

Advances in Information Retrieval

2023· book· en· W4327499178 on OpenAlexfundno aff
Jaap Kamps, Lorraine Goeuriot, Fábio Crestani, Maria Maistro, Hideo Joho, Brian Davis, Cathal Gurrin, Udo Kruschwitz, Annalina Caputo

Bibliographic record

VenueLecture notes in computer science · 2023
Typebook
Languageen
FieldComputer Science
TopicText and Document Classification Technologies
Canadian institutionsnot available
FundersLeibniz-GemeinschaftUniversity of Massachusetts AmherstCity, University of LondonNational Institute of Standards and TechnologyLa Rochelle UniversitéNational Institute of InformaticsIndian Institute of Science Education and Research MohaliUniversity of Chinese Academy of SciencesNational Institute of Technology HamirpurUniversité de LyonMasarykova UniverzitaUral Federal UniversityUniversité de NantesStockholms UniversitetInstitut National des Sciences Appliquées de LyonUniversität Duisburg-EssenUniversità di PisaTechnische Universität BerlinBauhaus-Universität WeimarUniversidad de GranadaUniversità di BolognaUniversität RegensburgUniversitetet i BergenUniversity of WindsorTechnische Universität WienRadboud UniversiteitUniversity of WaterlooCommonwealth Scientific and Industrial Research OrganisationOrta Doğu Teknik ÜniversitesiInstituto Superior TécnicoUniversité de Bretagne OccidentaleTsinghua UniversityChinese Academy of SciencesBirkbeck, University of LondonUniversidade de LisboaRenmin University of ChinaUniversität InnsbruckTechnische Universiteit DelftRégion NormandieUniversity of MinnesotaTU Graz, Internationale Beziehungen und MobilitätsprogrammeUniversiteit UtrechtRMIT UniversityHaute école Spécialisée de Suisse OccidentaleUniversity of SouthamptonUniversidade da CoruñaIstituto di Scienza e Tecnologie dell'InformazioneUniversidad Nacional de Educación a DistanciaUniversiteit LeidenYork UniversityUniversity of GlasgowUniversität PassauUniversidade do PortoDublin City UniversityPolitecnico di TorinoFriedrich-Schiller-Universität JenaUniversitetet i StavangerUniversity of ReginaUniversité Grenoble AlpesUniversity of Southern MaineCentre National de la Recherche ScientifiqueUniversity of RoehamptonNorges Teknisk-Naturvitenskapelige UniversitetSun Yat-sen UniversityUniversiteit van AmsterdamUniversidade de Santiago de CompostelaUniversità degli Studi di CagliariAix-Marseille UniversitéUniversity of WolverhamptonUniversidade Federal de Minas GeraisChinese University of Hong KongAalborg UniversitetKaiser PermanenteIndian Institute of Technology KharagpurIndian Institute of ScienceQatar UniversityUniversidad Autónoma de MadridUniversity of TsukubaGeorgetown UniversityUniversitatea din BucureștiUniversità degli Studi di PadovaZürcher Hochschule für Angewandte WissenschaftenJohns Hopkins UniversityIndian National Science Academy
KeywordsComputer scienceInformation retrievalState (computer science)Artificial intelligenceProgramming language

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.008
Science and technology studies0.0010.001
Scholarly communication0.0050.007
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0560.075

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations25
Published2023
Admission routes1
Has abstractno

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