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Record W4381956096 · doi:10.1007/978-3-031-36272-9

Artificial Intelligence in Education

2023· book· en· W4381956096 on OpenAlexafffund

Bibliographic record

VenueLecture notes in computer science · 2023
Typebook
Languageen
FieldEngineering
TopicAdvanced Data Processing Techniques
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Information and Communications TechnologyUniversity of California, IrvineCalifornia State University, FullertonUniversidade Federal de AlagoasUniversity of Colorado BoulderU.S. ArmySingapore Management UniversityStockholms UniversitetUniversity of Massachusetts AmherstUniversidade Federal do Rio de JaneiroUniversidade Federal de PernambucoUniversity of TsukubaTechnion-Israel Institute of TechnologyAteneo de Manila UniversityUniversidad de ChileUniversity of PittsburghHacettepe ÜniversitesiIran Telecommunication Research CenterUniversidad Autónoma de MadridUniversity of PennsylvaniaGeorgia Institute of TechnologyInstituto Tecnológico y de Estudios Superiores de MonterreySimon Fraser UniversityLeibniz-GemeinschaftSapienza Università di RomaGottfried Wilhelm Leibniz Universität HannoverSorbonne UniversitéUniversidad Politécnica de MadridUniversidade Federal de UberlândiaTurun YliopistoU.S. Army Combat Capabilities Development CommandAthabasca UniversityUniversity of MinnesotaUniversiteit UtrechtTrinity College DublinUniversidad del CaucaUniversity of SussexUniversidade Federal do Rio Grande do SulUniversity of AlbertaUniversidad Nacional de Educación a DistanciaArizona State UniversityUniversitat Pompeu FabraUniversité de LorraineNorth Carolina State UniversityCarnegie Mellon UniversityUniversité de LyonUniversity of Technology SydneyUniversity of Central FloridaUniversity of Southern CaliforniaUniversity of MemphisUniversità degli Studi di CagliariUniversity of South AustraliaMcGill UniversityBeijing Normal UniversityVanderbilt UniversityEuskal Herriko UnibertsitateaKanazawa UniversityUniversiteit van AmsterdamGeorgia State UniversityUniversity of Illinois at Urbana-ChampaignColorado State UniversityKindai UniversityÉcole Polytechnique Fédérale de LausanneValparaiso UniversityNorthern Illinois UniversityEducational Testing ServiceNorthern Kentucky UniversityUniversity College London
KeywordsComputer scienceArtificial intelligence

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0670.028

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 head0.021
GPT teacher head0.295
Teacher spread0.274 · 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
GenreEmpirical

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

Citations24
Published2023
Admission routes2
Has abstractno

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