{"id":"W1967177328","doi":"10.1177/154193120805200613","title":"“Thanks for Pointing that Out.” Making Sarcasm Accessible for all","year":2008,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Subtitles and Audiovisual Media","field":"Arts and Humanities","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Toronto Rehabilitation Institute","keywords":"Sarcasm; Tone (literature); Modality (human–computer interaction); Intonation (linguistics); Closed captioning; Computer science; Mood; Psychology; Multimedia; Speech recognition; Human–computer interaction; Linguistics; Irony; Artificial intelligence; Social psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001378678,0.001324214,0.0004840714,0.000645765,0.003519721,0.001985683,0.0008009231,0.001772337,0.1147213],"category_scores_gemma":[0.007281584,0.000324977,0.0006152857,0.0002961818,0.001170712,0.003748277,0.002481337,0.003303407,0.09186554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003777071,"about_ca_system_score_gemma":0.0005386662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680651,"about_ca_topic_score_gemma":0.003777609,"domain_scores_codex":[0.9990776,0.0003188196,0.00003735829,0.0001311355,0.0003068211,0.0001282794],"domain_scores_gemma":[0.9971437,0.000249094,0.0001888026,0.0003000066,0.001471367,0.0006470557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008453868,0.00006359978,0.00125739,0.000260917,0.00002175755,0.0004864221,0.0124427,0.00004080203,0.004607691,0.002494224,0.8674245,0.1108153],"study_design_scores_gemma":[0.00001163003,0.00009874486,0.002662304,0.0002101494,0.00002229012,0.001515839,0.01027347,0.0001241594,0.00109017,0.001216294,0.9827027,0.00007213003],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04879675,0.007514702,0.05662352,0.1702492,0.0506966,0.001054502,0.002975812,0.01297483,0.649114],"genre_scores_gemma":[0.09448628,0.00247806,0.02870353,0.0361052,0.002452941,0.0004153324,0.001009719,0.00197188,0.8323771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1147213,"threshold_uncertainty_score":0.383781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09915111864187293,"score_gpt":0.2857181906262283,"score_spread":0.1865670719843554,"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."}}