Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Venue2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
FundersU.S. Air Force AcademyU.S. Air ForceUniversity of Illinois at Urbana-ChampaignNazarbayev UniversityPontificia Universidad Católica del PerúIstituto Italiano di TecnologiaUniversität UlmUniversität BielefeldUniversitetet i OsloVysoké Učení Technické v BrněUniversiteit GentUniversity of TsukubaKungliga Tekniska HögskolanShizuoka UniversityChalmers Tekniska HögskolaUniversità degli Studi di Napoli Federico IIUniversity of AucklandAalborg UniversitetUniversiteit UtrechtYale UniversityUniversity of GlasgowUniversity of New South WalesHandong Global UniversityKeio UniversityScuola Superiore Sant'AnnaUniversità degli Studi di GenovaUniversity of WashingtonUniversità degli Studi di PadovaUniversity of MelbourneAmes Research CenterMacquarie UniversityToyota Research InstituteTechnische Universität BerlinUniversity of DenverCarnegie Mellon UniversityNational Aeronautics and Space AdministrationUniversité de SherbrookeNew Mexico State UniversityArizona State UniversityUniversità degli Studi di MilanoVirginia Polytechnic Institute and State UniversityHeriot-Watt University
KeywordsTable (database)Computer scienceDatabase
Abstract
fetched live from OpenAlexNo 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
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.
Teacher spread0.016 · 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
