{"id":"W2366754855","doi":"10.29173/cais375","title":"Convergence and Divergence in Tagging Systems: An Examination of Tagging Practices Over a Four Year Period","year":2013,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Period (music); Divergence (linguistics); Humanities; Convergence (economics); Library science; Geography; Computer science; Art; Linguistics; Philosophy; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001584856,0.0003736813,0.0007553492,0.0003992731,0.0001316534,0.002487016,0.002231711,0.0002292295,0.00004302482],"category_scores_gemma":[0.01378453,0.0003417737,0.0001253317,0.001030327,0.0007783183,0.03691482,0.001214743,0.0004004806,0.000002522559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001090056,"about_ca_system_score_gemma":0.0002451612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002726301,"about_ca_topic_score_gemma":0.00005587768,"domain_scores_codex":[0.9969479,0.0001408275,0.000980864,0.0006532726,0.000747049,0.0005300745],"domain_scores_gemma":[0.9679334,0.0002588897,0.003301218,0.000388553,0.02795228,0.00016573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007012923,0.0004970637,0.6152591,0.002895008,0.0001946887,0.000008303544,0.1161965,0.0001052858,0.09288596,0.1353304,0.0004766656,0.03608096],"study_design_scores_gemma":[0.0008704264,0.0008387875,0.7745004,0.003845842,0.0002447025,0.0001141408,0.01825695,0.1489596,0.03787733,0.007101206,0.00638371,0.001006957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933459,0.001099437,0.001791146,0.001580816,0.0002326807,0.0006682499,0.00003873698,0.00005275517,0.001190273],"genre_scores_gemma":[0.9921123,0.0007370785,0.006489259,0.00006475609,0.00005821206,0.00005648419,0.000001316711,0.00002356957,0.0004570549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1592413,"threshold_uncertainty_score":0.9999034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0375329282694167,"score_gpt":0.2798403217363502,"score_spread":0.2423073934669335,"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."}}