{"id":"W4318245003","doi":"10.3765/elm.2.5389","title":"Informational content vs. discourse orientation: experimental and computational perspectives","year":2023,"lang":"en","type":"article","venue":"Experiments in Linguistic Meaning","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Université du Québec à Montréal","keywords":"Linguistics; Meaning (existential); Semantics (computer science); Content (measure theory); Computer science; Computational linguistics; Psychology; Computational model; Natural language processing; Artificial intelligence; Mathematics; Philosophy","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":[0.01001292,0.0008521777,0.0006199699,0.0009251178,0.0006263992,0.003206583,0.001510063,0.001620615,0.006830628],"category_scores_gemma":[0.06781933,0.0006848521,0.0004374234,0.0008518955,0.00417125,0.006548779,0.002115966,0.001598629,0.0006276192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006569486,"about_ca_system_score_gemma":0.0004275167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009401974,"about_ca_topic_score_gemma":0.000547037,"domain_scores_codex":[0.989038,0.007928682,0.0005712302,0.001181128,0.0009846122,0.000296319],"domain_scores_gemma":[0.8567895,0.1302528,0.004227563,0.006497637,0.001632635,0.0005998889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01072032,0.0140646,0.09091767,0.007122185,0.001275515,0.0008521352,0.02860895,0.03086767,0.3723203,0.242764,0.005086032,0.1954006],"study_design_scores_gemma":[0.001871521,0.01020385,0.1531117,0.00065132,0.001107635,0.001957245,0.01632585,0.124892,0.229439,0.438471,0.02119315,0.0007757269],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8838549,0.001794198,0.07065354,0.002713875,0.0001426109,0.0007015019,0.001195028,0.0003926971,0.03855165],"genre_scores_gemma":[0.9743736,0.0004808238,0.02232449,0.0004881094,0.0001030075,0.0007623589,0.0005550148,0.0001159651,0.0007967414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01001292,"threshold_uncertainty_score":0.05295402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03544889131103577,"score_gpt":0.3524142309365706,"score_spread":0.3169653396255348,"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."}}