{"id":"W2011430509","doi":"10.1002/bult.193","title":"Recognizing Digital Genre","year":2001,"lang":"en","type":"article","venue":"Bulletin of the American Society for Information Science and Technology","topic":"Media, Communication, and Education","field":"Social Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Newspaper; Salient; Computer science; Disk formatting; Class (philosophy); Set (abstract data type); Meaning (existential); Identity (music); Typeface; Linguistics; Downtown; Information retrieval; Natural language processing; Artificial intelligence; History; Psychology; Advertising","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001252644,0.0008197243,0.0004721512,0.008956504,0.00143305,0.006024033,0.0008354728,0.001178941,0.02481283],"category_scores_gemma":[0.01019203,0.0002428114,0.0006206287,0.004575143,0.0007467132,0.006617242,0.003019281,0.001040194,0.0195186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071586,"about_ca_system_score_gemma":0.0007558503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003626189,"about_ca_topic_score_gemma":0.005252362,"domain_scores_codex":[0.9987025,0.0001777597,0.0001444546,0.0003645949,0.0004375218,0.0001731978],"domain_scores_gemma":[0.9954426,0.001047903,0.0003960082,0.0007045115,0.001925689,0.0004832321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002380361,0.00008909369,0.02591138,0.0008723762,0.00003393647,0.0007737248,0.00679687,0.0003850528,0.007841109,0.03232799,0.1305548,0.7941756],"study_design_scores_gemma":[0.0000237771,0.0001003996,0.0374504,0.0007615607,0.00006704012,0.002914491,0.009577688,0.006904168,0.005246073,0.02385236,0.913004,0.00009804512],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1211978,0.008534441,0.1075786,0.003789451,0.006588701,0.001121621,0.01719263,0.006016394,0.7279804],"genre_scores_gemma":[0.5572205,0.008972355,0.1867891,0.003123891,0.003611854,0.0009358748,0.0354369,0.001748157,0.2021613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.993976,"threshold_uncertainty_score":0.08300722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107169779089875,"score_gpt":0.3007226934075942,"score_spread":0.2796509956166955,"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."}}