{"id":"W3008018357","doi":"10.22230/cjc.2020v45n1a3765","title":"Article Usage Analytics for the Canadian Journal of Communication 2015–2018: A Guide for Authors, Publishers, and Readers","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Subject matter; Subject (documents); Presentation (obstetrics); Context (archaeology); Analytics; Style (visual arts); Library science; Computer science; Data science; History; Sociology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01558249,0.001796187,0.001310796,0.05588354,0.005559673,0.01322489,0.002151685,0.00104767,0.04922256],"category_scores_gemma":[0.09336185,0.001130947,0.00131609,0.05417869,0.001704606,0.006331567,0.003600726,0.002918802,0.03595035],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01669323,"about_ca_system_score_gemma":0.0638148,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4507594,"about_ca_topic_score_gemma":0.6717368,"domain_scores_codex":[0.9855107,0.001612922,0.001966136,0.0008555982,0.0091785,0.0008760854],"domain_scores_gemma":[0.8706188,0.03053048,0.009191638,0.004876249,0.07747743,0.007305444],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00005083109,0.00005769608,0.007641989,0.001195413,0.00002300852,0.0001008984,0.003602093,0.0002855628,0.0008364656,0.004776879,0.8116808,0.1697484],"study_design_scores_gemma":[0.00001503506,0.00002999912,0.02108357,0.001098927,0.00001886225,0.0001374774,0.002897954,0.0007729704,0.0005642016,0.002445474,0.9707862,0.0001494593],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02064234,0.01181809,0.05470547,0.02592423,0.003338531,0.007547519,0.6474606,0.03660312,0.1919601],"genre_scores_gemma":[0.0627811,0.02011697,0.4610332,0.005436944,0.002272916,0.01366456,0.3075261,0.01350991,0.1136583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9844175,"threshold_uncertainty_score":0.8962715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1229978471664857,"score_gpt":0.3796733514326077,"score_spread":0.256675504266122,"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."}}