{"id":"W2109343132","doi":"10.1177/0022022114555764","title":"A Typological and Probabilistic Approach for Exploring Cross-Cultural Differences","year":2014,"lang":"en","type":"article","venue":"Journal of Cross-Cultural Psychology","topic":"Cultural Differences and Values","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Probabilistic logic; Latent class model; Homogeneous; Flexibility (engineering); Latent variable model; Class (philosophy); Latent variable; Computer science; Statistical model; Econometrics; Psychology; Mathematics; Artificial intelligence; Machine learning; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.02241491,0.001298013,0.001309963,0.01153603,0.002442992,0.00534942,0.002829758,0.001782195,0.009141933],"category_scores_gemma":[0.06532346,0.0008194423,0.002549644,0.01251249,0.006272868,0.01076075,0.005156968,0.003194974,0.0006583874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004189724,"about_ca_system_score_gemma":0.002807467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00348172,"about_ca_topic_score_gemma":0.00440941,"domain_scores_codex":[0.9738227,0.01953548,0.001150338,0.002344512,0.002790042,0.0003568781],"domain_scores_gemma":[0.9378231,0.04692466,0.005352888,0.006626891,0.002666147,0.0006063259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00006285218,0.000176891,0.02103648,0.000487948,0.000500011,0.000203381,0.007929432,0.01189249,0.0004673611,0.8591859,0.001434295,0.09662285],"study_design_scores_gemma":[0.00002847183,0.00007739054,0.009947487,0.0002579485,0.00008278725,0.0003135994,0.004878071,0.04630608,0.0003099001,0.9261863,0.01151349,0.00009850543],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01609509,0.0004220932,0.9707247,0.0007152358,0.00007282298,0.0004207218,0.0003787572,0.0001110969,0.01105948],"genre_scores_gemma":[0.3670518,0.0007539084,0.6257669,0.0004196108,0.0001142599,0.003632393,0.0006576113,0.0000683319,0.001535337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02241491,"threshold_uncertainty_score":0.1185428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3170355653835869,"score_gpt":0.489202396864593,"score_spread":0.1721668314810061,"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."}}