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Record W4387345067 · doi:10.1145/3610200

Perspectives: Creating Inclusive and Equitable Hybrid Meeting Experiences

2023· article· en· W4387345067 on OpenAlex

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

VenueProceedings of the ACM on Human-Computer Interaction · 2023
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsMicrosoft (Canada)
Fundersnot available
KeywordsMode (computer interface)Computer scienceKey (lock)Human–computer interactionSpace (punctuation)Universal designWork (physics)Natural (archaeology)MultimediaArchitectural engineeringEngineeringWorld Wide WebComputer securityMechanical engineering

Abstract

fetched live from OpenAlex

With the shift to hybrid meetings in work spaces, there is an increasing need to create a more inclusive hybrid meeting experience where people meeting together in a room interact with those joining remotely. This paper describes a design exploration, implementation, and evaluation of Perspectives, a novel hybrid meeting system that aimed to create an inclusive and equitable space for hybrid meetings. Perspectives digitally composites everyone into a virtual room so that each person has a unique but spatially consistent viewpoint into the meeting. The user study compared Perspectives with three commercially available UX designs for hybrid meetings: Gallery, Together Mode, and Front Row. Results from this study revealed key benefits of Perspectives, including supporting natural interactions, creating a strong sense of co-presence, and reducing cognitive load. Results from the study also helped iterate on the design principles of Perspectives, which offer important insights on supporting hybrid meetings.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0020.004
Research integrity0.0000.000
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 head0.039
GPT teacher head0.336
Teacher spread0.297 · 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