{"id":"W2587346429","doi":"10.17485/ijst/2016/v9i48/109085","title":"Emotion Recognition in Different Cultures","year":2016,"lang":"en","type":"article","venue":"Indian Journal of Science and Technology","topic":"Technology and Human Factors in Education and Health","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Psychology; Emotional intelligence; Cultural intelligence; Collectivism; Emotion classification; Emotional expression; Facial expression; Cognition; Cognitive psychology; Set (abstract data type); Test (biology); Facial recognition system; Perception; Scale (ratio); Ethnic group; Cross-cultural; Face (sociological concept); Social psychology; Individualism; Computer science; Pattern recognition (psychology); Communication; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001859899,0.0003060157,0.000333253,0.0007249466,0.0006145161,0.002158132,0.0003888099,0.0004155443,0.003127931],"category_scores_gemma":[0.006910537,0.0001098087,0.0004504227,0.000790529,0.001526696,0.001072924,0.00164901,0.0006087163,0.000342577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006746195,"about_ca_system_score_gemma":0.0004150834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001785846,"about_ca_topic_score_gemma":0.0007329328,"domain_scores_codex":[0.9984596,0.0005578825,0.0001729252,0.0002745956,0.0003455818,0.000189462],"domain_scores_gemma":[0.9965485,0.001282115,0.000676315,0.0004027298,0.0008059599,0.0002844425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00102253,0.0004097846,0.4313903,0.001434716,0.0004984893,0.004700621,0.0942898,0.001643996,0.01884419,0.02286208,0.004123978,0.4187794],"study_design_scores_gemma":[0.00003830075,0.0005094487,0.8515339,0.0006660677,0.0003539725,0.009320896,0.07896376,0.002308964,0.01217484,0.01684599,0.02714776,0.0001360864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474844,0.004132088,0.003472363,0.0009428812,0.000197327,0.00004935965,0.0001381759,0.00002673521,0.04355676],"genre_scores_gemma":[0.9974769,0.0007161885,0.0005450353,0.0001901173,0.00003019627,0.00002224475,0.00004470122,0.000005813679,0.0009687179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003127931,"threshold_uncertainty_score":0.01046395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133901205679563,"score_gpt":0.3210334125582786,"score_spread":0.2996944005014829,"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."}}