{"id":"W3215288638","doi":"10.3390/su132313188","title":"The Landscape of Risk Perception Research: A Scientometric Analysis","year":2021,"lang":"en","type":"article","venue":"Sustainability","topic":"Risk Perception and Management","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Risk governance; Data science; Risk perception; Natural hazard; Focus group; Thematic analysis; Bibliometrics; Scientometrics; Perception; Corporate governance; Geography; Knowledge management; Qualitative research; Psychology; Sociology; Computer science; Business; Social science; Marketing; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0535125,0.001252762,0.003255937,0.1758509,0.002894363,0.01829791,0.001326951,0.001490933,0.005042559],"category_scores_gemma":[0.1586866,0.0006331978,0.003341,0.2283816,0.003127058,0.01148805,0.005466824,0.001466474,0.0009717011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004434106,"about_ca_system_score_gemma":0.006280655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003699547,"about_ca_topic_score_gemma":0.003232458,"domain_scores_codex":[0.9472575,0.02142618,0.00662544,0.003322563,0.02044122,0.0009271005],"domain_scores_gemma":[0.8149943,0.1368089,0.01245533,0.01259398,0.02131868,0.001828792],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001909037,0.0001960622,0.170606,0.01510896,0.002944191,0.0006296071,0.01008308,0.005637159,0.001932258,0.0800949,0.0265957,0.6859812],"study_design_scores_gemma":[0.00008072796,0.00055699,0.4379704,0.009941227,0.001794147,0.001884695,0.02306238,0.0359419,0.002659258,0.2164001,0.2692268,0.0004813341],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.403151,0.1503492,0.206438,0.04185193,0.001694917,0.003859762,0.04436468,0.003168674,0.145122],"genre_scores_gemma":[0.777167,0.053482,0.1418406,0.001310893,0.001800818,0.003229642,0.01699966,0.000535842,0.003633537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9464875,"threshold_uncertainty_score":0.2830045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05429217728800833,"score_gpt":0.4345194863785171,"score_spread":0.3802273090905087,"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."}}