{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003546259,0.0002264791,0.0003241253,0.0000315055,0.001416067,0.0001767209,0.0003015486,0.00008847644,0.00001819965],"category_scores_gemma":[0.0001047745,0.0001640789,0.0004084689,0.000020631,0.0002874362,0.0004155304,0.0001919023,0.0001480266,4.944863e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000041429,"about_ca_system_score_gemma":0.00001861504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004959283,"about_ca_topic_score_gemma":0.00003614098,"domain_scores_codex":[0.9988641,0.000003110655,0.00033294,0.0002893788,0.0001225638,0.0003878734],"domain_scores_gemma":[0.9990481,0.0001752885,0.0004382985,0.00006671282,0.0002190393,0.00005253388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001901631,0.0001949955,0.1003314,0.002036778,0.0005962124,1.498488e-7,0.747772,0.00001084962,0.01001503,0.08996519,0.04766102,0.001226198],"study_design_scores_gemma":[0.004804696,0.0008969383,0.02091433,0.00192596,0.0007170658,0.00001276425,0.4769628,0.001996557,0.07494355,0.02200745,0.3921136,0.002704244],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958433,0.0001601139,0.00001191749,0.0001468366,0.0004569433,0.000432336,0.0001068117,0.00004715314,0.002794622],"genre_scores_gemma":[0.9966924,0.00004645857,0.0009624673,0.0002392529,0.0007872981,0.00003642391,0.000007433813,0.00004590135,0.001182362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3444526,"threshold_uncertainty_score":0.9998839,"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."}}