{"id":"W3096268605","doi":"10.3138/jsp.52.1.03","title":"An Analysis of Spam from Predatory Publications in Library and Information Science","year":2020,"lang":"en","type":"article","venue":"Journal of Scholarly Publishing","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subject (documents); Legitimacy; Library science; Computer science; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003334046,0.0003063754,0.0005536336,0.007333445,0.001302348,0.001305541,0.0004179825,0.0008574863,0.0009289304],"category_scores_gemma":[0.03704261,0.0001798329,0.0002765225,0.005024789,0.0007206553,0.001281719,0.0009783594,0.0006050195,0.0007374556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005853812,"about_ca_system_score_gemma":0.0006103136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001668156,"about_ca_topic_score_gemma":0.001770297,"domain_scores_codex":[0.9954076,0.002004123,0.0004687005,0.0003075389,0.001500147,0.0003118081],"domain_scores_gemma":[0.9096124,0.05536119,0.01565996,0.003628509,0.01360805,0.002129885],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009294281,0.00108122,0.8999848,0.0004419377,0.00008542656,0.001273953,0.01957671,0.0004859166,0.006258306,0.0005794609,0.002599577,0.06670326],"study_design_scores_gemma":[0.00001783481,0.0006981262,0.9755254,0.00006214373,0.00006463021,0.001913227,0.008445434,0.005558113,0.003213548,0.0004045966,0.004057682,0.00003932227],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980054,0.00009226352,0.000475486,0.00006919588,0.000008395504,0.00006083748,0.0002504524,0.00003184546,0.001006038],"genre_scores_gemma":[0.9956583,0.0001472716,0.001823754,0.00007322028,0.00006982046,0.0001074354,0.001130193,0.0000185735,0.0009715345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9991425,"threshold_uncertainty_score":0.01763231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01838097363728111,"score_gpt":0.2301992201931874,"score_spread":0.2118182465559063,"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."}}