{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001089882,0.00004522252,0.00008542438,0.00009013364,0.0009544656,0.00008180795,0.0006028106,0.00003860903,0.00001113515],"category_scores_gemma":[0.001416954,0.00003599201,0.00005579005,0.001502923,0.003880073,0.0003876481,0.0001072825,0.00006821788,0.00001116671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000624196,"about_ca_system_score_gemma":0.0002910215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002502394,"about_ca_topic_score_gemma":0.00001056584,"domain_scores_codex":[0.9992576,0.00001010302,0.000179207,0.00007575863,0.0002842776,0.0001930131],"domain_scores_gemma":[0.9987587,0.0001054792,0.000304119,0.000233225,0.000559932,0.00003855689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001158997,0.00003696723,0.01681773,0.00001814353,0.00001637859,1.024179e-8,0.03916988,0.000003093285,0.0002073485,0.03808983,0.03307719,0.8725519],"study_design_scores_gemma":[0.00008253392,0.00002838752,0.001771425,0.000008412009,0.00000454106,0.000002001375,0.09010843,0.00004818465,0.000127444,0.001837473,0.9059226,0.0000585603],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8539395,0.00009288435,0.001279983,0.109528,0.0002640684,0.0005104363,0.00001009812,0.0001226198,0.03425242],"genre_scores_gemma":[0.9940308,0.0007785302,0.003678317,0.001129194,0.00003122948,0.00002885599,0.000002609928,0.000002050147,0.000318359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8728454,"threshold_uncertainty_score":0.9988308,"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."}}