{"id":"W2890027215","doi":"10.3386/w21579","title":"Neophilia Ranking of Scientific Journals","year":2015,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranking (information retrieval); Journal ranking; Information retrieval; Data science; Computer science; Library science; Citation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007345106,0.0004180103,0.001283894,0.01442089,0.001933267,0.007052303,0.0008335527,0.00102037,0.007870657],"category_scores_gemma":[0.06139922,0.0002813181,0.000737229,0.009741903,0.001707409,0.004416717,0.001922526,0.0008206517,0.001225693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00335251,"about_ca_system_score_gemma":0.002141207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00201229,"about_ca_topic_score_gemma":0.00199511,"domain_scores_codex":[0.9909156,0.004129899,0.0006823174,0.0009852592,0.002560299,0.0007266889],"domain_scores_gemma":[0.9123419,0.05350967,0.01424068,0.005947911,0.01114614,0.002813773],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001279608,0.0003528023,0.2020894,0.00119235,0.0007676885,0.0008081234,0.002380029,0.05488927,0.006387568,0.4594033,0.03185338,0.2385965],"study_design_scores_gemma":[0.0002938996,0.0004269799,0.1483773,0.0002303988,0.000371922,0.0006476045,0.002194144,0.3129422,0.00647955,0.5042465,0.02353768,0.0002517875],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8251085,0.001776519,0.1048898,0.003829393,0.0003588923,0.0003640535,0.002886129,0.0008065457,0.05997996],"genre_scores_gemma":[0.9870867,0.0002215083,0.009545749,0.0001316411,0.0001336606,0.00007594485,0.0004916791,0.00003691112,0.00227617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9926549,"threshold_uncertainty_score":0.03884512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5146654059760064,"score_gpt":0.5025642595274311,"score_spread":0.0121011464485753,"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."}}