{"id":"W72549335","doi":"","title":"Automated Coding of Qualitative Interviews with Latent Semantic Analysis.","year":2007,"lang":"en","type":"article","venue":"WU Research","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University","keywords":"Coding (social sciences); Computer science; Latent semantic analysis; Natural language processing; Qualitative research; Qualitative analysis; Psychology; Artificial intelligence; Data science; Sociology; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00367623,0.00007310834,0.0001896014,0.0002143855,0.00005363197,0.00001029504,0.0001922077,0.0001049282,0.00003574532],"category_scores_gemma":[0.000355179,0.00004978412,0.00007060893,0.0007088887,0.0003954451,0.000001119659,0.0001171675,0.0001413576,0.000008025614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001050661,"about_ca_system_score_gemma":0.00004744953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001119604,"about_ca_topic_score_gemma":0.0002677159,"domain_scores_codex":[0.9986106,0.0003034928,0.0002148914,0.0002406882,0.0003209891,0.0003093681],"domain_scores_gemma":[0.9992396,0.0001409244,0.00005500273,0.0002590995,0.0002290066,0.00007637096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001115969,0.0004887411,0.02792966,0.0003743294,0.002936522,0.0000906452,0.01302644,0.00005843531,0.8911874,0.0006642882,0.01250593,0.0496217],"study_design_scores_gemma":[0.00282569,0.006523253,0.134346,0.0004190667,0.0003813404,0.00002974274,0.04608479,0.005675568,0.7741673,0.0003190656,0.02839326,0.0008349039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843572,0.0006352454,0.01306346,0.0002099716,0.00002018465,0.0001001175,0.000006087007,0.00002944539,0.00157833],"genre_scores_gemma":[0.99703,0.00006823544,0.002339389,0.00002408572,0.00002639381,0.000005213825,0.00003681084,0.000006805315,0.0004631133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.11702,"threshold_uncertainty_score":0.2030137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2042462830884539,"score_gpt":0.5100896614593289,"score_spread":0.3058433783708749,"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."}}