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Record W4327657211 · doi:10.1007/978-3-031-28244-7

Advances in Information Retrieval

2023· book· en· W4327657211 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
FundersUniversity of Massachusetts AmherstUniversität InnsbruckUniversität KonstanzUniversität LeipzigUniversität Duisburg-EssenUniversità di PisaWuhan UniversityUniversidade do PortoNational Institute of Standards and TechnologyTechnion-Israel Institute of TechnologyRheinische Friedrich-Wilhelms-Universität BonnUniversity of Cape TownSichuan UniversityUniversity of WaterlooInstitute of Computing Technology, Chinese Academy of SciencesUniversité de Caen NormandieUniversity of CreteRMIT UniversityTsinghua UniversityEdinburgh Napier UniversityAccentureRenmin University of ChinaUniversità degli Studi di Milano-BicoccaChinese Academy of SciencesTechnische Universiteit DelftUniversiteit van AmsterdamDublin City UniversityUniversity College LondonUniversity of EssexSorbonne UniversitéUniversità degli Studi di MilanoYork UniversityMontclair State UniversityNational Institute of Advanced Industrial Science and TechnologyUniversiteit LeidenUniversity of OtagoUniversité de NeuchâtelRégion NormandieUniversità degli Studi di UdineJohns Hopkins University
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: Methods · 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
GenreMethods

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

Citations10
Published2023
Admission routes1
Has abstractno

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