{"id":"W2197022017","doi":"10.33011/lilt.v12i.1375","title":"Distinguishing Voices in The Waste Land using Computational Stylistics","year":2015,"lang":"en","type":"article","venue":"Linguistic Issues in Language Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Stylistics; Viewpoints; Computer science; Interpretation (philosophy); Poetry; Cluster analysis; Natural language processing; Linguistics; Citation; Perspective (graphical); Segmentation; Artificial intelligence; Computational linguistics; Art; Visual arts; 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":[0.001546439,0.0003523408,0.0003197524,0.002927073,0.0012417,0.00421519,0.0004786101,0.0005852486,0.001393097],"category_scores_gemma":[0.0084511,0.0002437299,0.0005504133,0.001816264,0.002218991,0.003513463,0.001616092,0.0009054746,0.0005286157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006058929,"about_ca_system_score_gemma":0.0004723861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001331577,"about_ca_topic_score_gemma":0.002537191,"domain_scores_codex":[0.9987807,0.0006740824,0.00007058517,0.0002246107,0.0001891416,0.00006089995],"domain_scores_gemma":[0.9945529,0.004009623,0.0004833996,0.0005300888,0.0003181221,0.0001058958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007403464,0.0001293686,0.07019939,0.0005870535,0.0001506588,0.001601554,0.09708542,0.04019767,0.0448684,0.3046901,0.007786855,0.4319633],"study_design_scores_gemma":[0.00004389578,0.0001425023,0.05349884,0.0003032121,0.00008684561,0.001918499,0.03538497,0.4944334,0.02391207,0.3205865,0.06949225,0.0001970897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5760009,0.0004781859,0.403066,0.001174874,0.0001022456,0.0001193733,0.0005018545,0.0004250525,0.01813149],"genre_scores_gemma":[0.9389191,0.000148247,0.05861402,0.00006945943,0.00004782038,0.00004477698,0.0003556765,0.0001050811,0.001695956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00421519,"threshold_uncertainty_score":0.008178473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03122741613727188,"score_gpt":0.3270873528477606,"score_spread":0.2958599367104887,"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."}}