{"id":"W4225003922","doi":"10.1145/3491102.3501940","title":"VibEmoji: Exploring User-authoring Multi-modal Emoticons in Social Communication","year":2022,"lang":"en","type":"article","venue":"CHI Conference on Human Factors in Computing Systems","topic":"Digital Communication and Language","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); The Scarborough Hospital; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Modal; Human–computer interaction; Multimedia; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009518238,0.000251382,0.000379572,0.0004913781,0.0009505805,0.0005381368,0.002651542,0.00005890977,0.00001649206],"category_scores_gemma":[0.00005029125,0.0002824402,0.00007682181,0.0006120614,0.00006567447,0.0004650749,0.001614504,0.0008716879,0.000009951496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004350596,"about_ca_system_score_gemma":0.00007580153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004178781,"about_ca_topic_score_gemma":0.0001660982,"domain_scores_codex":[0.9971485,0.0008549484,0.0006650402,0.0004814638,0.0004225996,0.0004274445],"domain_scores_gemma":[0.998245,0.0002472686,0.0002871652,0.00109514,0.00005156199,0.00007385914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006153032,0.0005393439,0.04090098,0.00005508601,0.00001664142,0.00001724339,0.03658551,0.007678254,0.0004632549,0.9112175,0.00006610786,0.002453911],"study_design_scores_gemma":[0.003473644,0.0003464707,0.3236317,0.001042599,0.000008039819,0.00002180582,0.03327663,0.6302294,0.0003435976,0.001707541,0.004009763,0.001908787],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816059,0.00007695705,0.005873613,0.0002451017,0.0004196077,0.0003980479,0.000006218799,0.0003025216,0.01107199],"genre_scores_gemma":[0.9991341,0.000002927364,0.0005242901,0.0000625685,0.00002712108,0.00005643614,0.0000289945,0.00002166982,0.0001419131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.90951,"threshold_uncertainty_score":0.9999627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.276257002162567,"score_gpt":0.3604159060586858,"score_spread":0.08415890389611885,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}