MétaCan
Menu
Back to cohort
Record W3186941039 · doi:10.1145/3477315.3477316

Overview of ASSETS 2020

2021· article· en· W3186941039 on OpenAlex
Hugo Nicolau, Karyn Moffatt, Tiago Guerreiro

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM SIGACCESS Accessibility and Computing · 2021
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsMcGill University
Fundersnot available
KeywordsAttendanceGlobeMainstreamInclusion (mineral)Computer scienceDiversity (politics)World Wide WebLibrary sciencePolitical scienceSociologyMedicineSocial science

Abstract

fetched live from OpenAlex

In October 2020 was the 22nd edition of the ACM SIGACCESS Conference on Computers and Accessibility(ASSETS 2020), which took place online. The ASSETS conference is the premier computing research conferenceexploring the design, evaluation, and use of computing and information technologies to benefi t people withdisabilities and older adults. This year, the ASSETS conference continued its tradition of presenting innovativeresearch on mainstream and specialized assistive technologies, accessible computing, and assistive applicationsof computer, network, and information technologies. We set a new attendance record with 395 attendees from29 countries from all continents across the globe. Our organization and program committees were open tonominations from the community. We had 50 people attending the conference with the support of theSIGACCESS Diversity and Inclusion Scholarships.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.180
GPT teacher head0.504
Teacher spread0.324 · 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