{"id":"W3121207479","doi":"10.1093/pan/mpn007","title":"Lexical Cohesion Analysis of Political Speech","year":2008,"lang":"en","type":"article","venue":"Political Analysis","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Cohesion (chemistry); Rhetorical question; Computer science; Linguistics; Annotation; Natural language processing; Lexical density; Representation (politics); Politics; Interpretation (philosophy); Artificial intelligence; Lexical item; Political science","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":[0.0002783336,0.0002021087,0.0008296759,0.001559812,0.0001210264,0.00004939195,0.001008531,0.000162398,0.0001405423],"category_scores_gemma":[0.000379552,0.0001629993,0.0008784969,0.007250839,0.0003469047,0.0002451231,0.0003413831,0.0002377638,0.00002100137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001187056,"about_ca_system_score_gemma":0.00008474261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001269434,"about_ca_topic_score_gemma":0.00003999663,"domain_scores_codex":[0.9971699,0.0001405094,0.0005539381,0.0005561779,0.0007472822,0.0008322498],"domain_scores_gemma":[0.9979039,0.0003526481,0.00009932674,0.0008667707,0.0002764642,0.0005008201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004359377,0.0001361713,0.02449476,0.000008696272,0.001561392,0.00007613922,0.00004428296,0.000009261997,0.0003214925,0.9727145,0.00005687929,0.0005720542],"study_design_scores_gemma":[0.0004336834,0.0002678706,0.1595519,0.00002841453,0.0193914,0.0001107306,0.0000701035,0.4229709,0.119026,0.2766726,0.0002199496,0.001256457],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123877,0.0002829972,0.8691189,0.00289734,0.00003210805,0.0000712792,0.00002439685,0.0004521249,0.003243939],"genre_scores_gemma":[0.8316082,0.000003031381,0.1675486,0.0006388375,0.00004115322,0.000003876722,0.00001941847,0.000006177046,0.0001306678],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7077312,"threshold_uncertainty_score":0.664692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193931203427257,"score_gpt":0.3055167210051506,"score_spread":0.283577408970878,"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."}}