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Record W4380187447 · doi:10.1007/978-3-031-35445-8

Information Management and Big Data

2023· book· en· W4380187447 on OpenAlexfundno aff
Juan Antonio Lossio-Ventura, Jorge Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas

Bibliographic record

VenueCommunications in computer and information science · 2023
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
FundersNational Institute of Mental HealthInstituto Superior TécnicoLeibniz-GemeinschaftUniversity of Illinois at Urbana-ChampaignNational Institutes of HealthInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementÉcole Nationale du Génie de l'Eau et de l'Environnement de StrasbourgUniversidade Estadual de MaringáUniversidad de OviedoLa Rochelle UniversitéUniversidade de CoimbraGuangzhou UniversityPontificia Universidad Católica del PerúUniversidade Federal do AmazonasUniversidad de la República UruguayUniversidade Federal de Juiz de ForaBarcelona Supercomputing CenterUniversidad Nacional del Centro de la Provincia de Buenos AiresUniversité Lumière Lyon 2Università di BolognaUniversidad de ChileUniversidade de LisboaUniversidad Autónoma de TamaulipasUniversidade de São PauloUniversité de LilleKing Abdulaziz UniversityUniversidad Politécnica de MadridUniversity of TorontoUniversitat de ValènciaUniversitat Politècnica de ValènciaUniversité de MontpellierUniversity of Technology SydneyNewcastle UniversityPontifícia Universidade Católica do Rio de JaneiroMissouri University of Science and TechnologyUniversidade Federal do ABCUniversitetet i OsloUniversidad Peruana Cayetano HerediaUniversity of OxfordVrije Universiteit AmsterdamUniversidad del PacíficoLiverpool Hope UniversityInstitut "Jožef Stefan"London School of Hygiene and Tropical MedicineAix-Marseille UniversitéUniversität WienUniversitat Rovira i VirgiliPolitecnico di TorinoUniversidad Michoacana de San Nicolás de HidalgoUniversidad Nacional de CórdobaGovind Ballabh Pant University of Agriculture and TechnologyCentre de Coopération Internationale en Recherche Agronomique pour le DéveloppementNorges Teknisk-Naturvitenskapelige UniversitetUniversité de StrasbourgUniversidade de BrasíliaUniversidade Federal do Rio de JaneiroUniversity of Ottawa
KeywordsBig dataComputer scienceInformation retrievalData scienceData mining

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)0.001
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0010.003
Scholarly communication0.0070.012
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0340.020

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 head0.183
GPT teacher head0.330
Teacher spread0.147 · 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

Citations2
Published2023
Admission routes1
Has abstractno

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