{"id":"W4244994113","doi":"10.13021/g89k55","title":"The Journal Impact Factor and its discontents: steps toward responsible metrics and better research assessment","year":2016,"lang":"en","type":"article","venue":"Open Scholarship Initiative Proceedings","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Impact factor; Citation; Scholarship; Declaration; Citation impact; Quality (philosophy); Political science; Metric (unit); Public relations; Computer science; Library science; Business; Marketing; Law","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":["metaresearch","bibliometrics","sts","scholarly_communication"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.1215911,0.0002792865,0.0004491794,0.02154365,0.002514084,0.03172173,0.004633921,0.0001710389,0.0003416922],"category_scores_gemma":[0.143427,0.0001186543,0.00009652319,0.05863228,0.0005421138,0.01009371,0.004936514,0.001548176,0.000106073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005310375,"about_ca_system_score_gemma":0.0008010161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002166727,"about_ca_topic_score_gemma":0.00000692776,"domain_scores_codex":[0.9842398,0.001350059,0.0009877787,0.0009945446,0.01106267,0.001365146],"domain_scores_gemma":[0.9580386,0.02404879,0.0005555999,0.0003577408,0.01581063,0.00118867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003981291,0.00008759237,0.7641395,0.000007588684,0.00008861891,0.00001451263,0.0007980437,1.796435e-8,0.0122882,0.003403199,0.006529838,0.2122448],"study_design_scores_gemma":[0.001419203,0.001012827,0.9328616,0.00009731407,0.000009725574,0.00006793531,0.003865566,0.00007356823,0.003109208,0.04655265,0.01065769,0.0002727266],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757965,0.001851245,0.0003236462,0.01464298,0.0001661936,0.0008848554,0.00009440323,0.00001023242,0.006229923],"genre_scores_gemma":[0.993786,0.003284597,0.0007750962,0.0002359388,0.0001007827,0.00003804826,4.379802e-7,0.00002522142,0.001753878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2119721,"threshold_uncertainty_score":0.9987845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9026090047239823,"score_gpt":0.6611413626244598,"score_spread":0.2414676420995225,"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."}}