MétaCan
Menu
Back to cohort
Record W3205870165 · doi:10.1145/3479518

Meeting with Media: Comparing Synchronous Media Sharing and Icebreaker Questions in Initial Interactions via Video Chat

2021· article· en· W3205870165 on OpenAlexaff
Matthew K. Miller, Regan L. Mandryk

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsConversationComputer scienceContext (archaeology)MultimediaDigital mediaSocial mediaWorld Wide WebPsychologyCommunication

Abstract

fetched live from OpenAlex

Video chat continues to play an increasing role in the personal and professional lives of many people. When meeting someone for the first time via video chat, traditional icebreakers such as discussion questions may be used to ease conversation and facilitate disclosure; however, icebreaker questions have been shown to be less effective at facilitating relationships for some people than others. In this paper, we propose synchronously sharing online media as a more flexible, robust, and effective method of facilitating initial interactions in video chat. Our comparative study of icebreaker questions and media sharing reveals that shared media supports a different style of conversation than icebreakers, but results in equal levels of self-disclosure and trust along with higher levels of warranting and relatedness. Further, while the effectiveness of icebreakers in building trust is lower for individuals low in agreeableness, media sharing results in similar trust formation at all levels of agreeableness. Synchronous media sharing is a promising way of leveraging the digital context of video chat to better support early stages of relationships.

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.

How this classification was reachedexpand

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.049
GPT teacher head0.337
Teacher spread0.288 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ACM on Human-Computer InteractionSame topicDigital Marketing and Social MediaFrench-language works237,207