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CHI’25:Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
Naomi Yamashita, Vanessa Evers, Koji Yatani, Xianghua Ding, Bongshin Lee, Marshini Chetty, Phoebe Toups-Dugas
Bibliographic record
VenueUniversity of Twente Research Information · 2025
Typebook
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsnot available
FundersInstitut des Systèmes Intelligents et de Robotique, Université Pierre et Marie CurieLeibniz-GemeinschaftTélécom ParisCity, University of LondonCommunication University of ChinaUniversität KonstanzUniversity of Illinois at Urbana-ChampaignUniversität StuttgartStockholms UniversitetIndian Institute of Technology KharagpurUniversiti Malaysia SarawakKorea Advanced Institute of Science and TechnologySingapore University of Technology and DesignUniversität Duisburg-EssenUniversità degli Studi di TorinoInstitut National Du CancerOulun YliopistoDeepMindUniversidade de LisboaGöteborgs UniversitetNorthumbria UniversityUniversità degli Studi di MilanoInstitut Polytechnique de ParisUniversität SalzburgGeorge Mason UniversityUniversität des SaarlandesNational and Kapodistrian University of AthensTechnische Universität WienLudwig-Maximilians-Universität MünchenUniversity of WaterlooSorbonne UniversitéKungliga Tekniska HögskolanGeorgia Institute of TechnologyUniversity of NottinghamUniversitetet i OsloUniversität zu LübeckGottfried Wilhelm Leibniz Universität HannoverFondazione Bruno KesslerLinköpings UniversitetKeio UniversityEidgenössische Technische Hochschule ZürichUniversität UlmChalmers Tekniska HögskolaUniversity of WashingtonAalborg UniversitetUniversité Paris-SaclayAalto-YliopistoInstituto Superior TécnicoGwangju Institute of Science and TechnologySun Yat-sen UniversityUniversity of Wisconsin-MadisonUniversiteit van AmsterdamQueensland University of TechnologyMonash UniversityMacalester CollegeUniversity of BristolTrinity College DublinNanyang Technological UniversityFogarty International CenterNational University of SingaporeTechnische Universität Bergakademie FreibergSwansea UniversitySyracuse UniversityCardiff UniversityUniversity of PittsburghLoughborough UniversityUniversity of SussexEuropean CommissionTechnische Universiteit EindhovenTechnische Universität DarmstadtNewcastle UniversityUniversity of TorontoKing's College LondonImperial College LondonUniversity of GlasgowYork UniversityKU LeuvenPomona CollegeBentley UniversityUniversiteit UtrechtHeriot-Watt UniversityHonda Research Institute, USAVictoria University of WellingtonAarhus UniversitetUniversität SiegenEmory UniversityUniversität LeipzigUniversidad Carlos III de MadridTsinghua UniversityMarquette UniversityUniversity of Central LancashireCentre National de la Recherche ScientifiqueEwha Womans UniversityPolitecnico di TorinoRobert Gordon UniversityWestern Washington UniversityUniversität St. GallenUniversity of OxfordUniversity College DublinMicrosoft ResearchMcGill UniversityUniversity of DundeeEurostarsUniversity of Northern British ColumbiaVictoria UniversityClemson UniversityUniversity of CanterburyDartmouth CollegeDrexel UniversityWellesley CollegeUniversity of UtahNvidiaUniversität BremenCarnegie Mellon UniversityAllen Institute for Artificial IntelligenceUniversity of ReginaAlbert-Ludwigs-Universität FreiburgUniversity of Colorado BoulderUniversity College LondonPurdue UniversityArizona State UniversityYale UniversityTechnische Universiteit DelftSyddansk UniversitetHarvard University
KeywordsInformation systemKey (lock)Set (abstract data type)Identification (biology)Government (linguistics)
DOInot available
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.005
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 score0.192
Threshold uncertainty score0.642
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.192 | 0.077 |
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 head0.128
GPT teacher head0.399
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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".
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Citations0
Published2025
Admission routes1
Has abstractyes