{"id":"W2237841574","doi":"10.1007/1-4020-4102-0_5","title":"The Subjectivity of Lexical Cohesion in Text","year":2006,"lang":"en","type":"book-chapter","venue":"The information retrieval series","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Subjectivity; Cohesion (chemistry); Linguistics; Perception; Computer science; Lexical choice; Natural language processing; Lexical item; Psychology; Artificial intelligence; Epistemology; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007172434,0.0002394229,0.0003012221,0.0001381528,0.0002115089,0.00007452686,0.0002582754,0.0004056653,0.0006485303],"category_scores_gemma":[0.00004784682,0.0001452238,0.0001655816,0.0001210165,0.0003353009,0.0003996044,0.00005764357,0.0004710366,0.0004526466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006919295,"about_ca_system_score_gemma":0.00006228164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002405274,"about_ca_topic_score_gemma":0.000460956,"domain_scores_codex":[0.9984033,0.00009000652,0.0006931858,0.0001393147,0.0004453687,0.0002288165],"domain_scores_gemma":[0.9985537,0.0002834132,0.0005280122,0.0004263758,0.000177043,0.00003143416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005364203,0.0001041334,0.000285862,0.0002252059,0.0002497869,0.00001698057,0.008936073,0.00001617464,0.00007816801,0.8313102,0.03269385,0.1207194],"study_design_scores_gemma":[0.001109281,0.0003933586,0.01485196,0.0001253691,0.0001210108,0.00006021698,0.002231159,0.00003392634,0.0005953328,0.04683156,0.9331402,0.0005065999],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.006360674,0.0006927221,0.0003313886,0.0002863675,0.0008560835,0.0006531387,0.0001179289,0.00006113951,0.9906406],"genre_scores_gemma":[0.6381968,0.0002374598,0.00001198538,0.0002919153,0.0002731425,0.00002503369,0.0003714703,0.00003443181,0.3605578],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9004464,"threshold_uncertainty_score":0.7100951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0132768262315947,"score_gpt":0.2497378528602038,"score_spread":0.2364610266286091,"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."}}