{"id":"W2338954654","doi":"","title":"Cross-Cultural Comparison Between 7-Eleven and KEDI","year":2016,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ostensive definition; Logos Bible Software; White (mutation); Commodity; Selection (genetic algorithm); China; Diversity (politics); Advertising; Logo (programming language); Cross-cultural; Cultural diversity; Aesthetics; Sociology; Business; Political science; Linguistics; Computer science; Artificial intelligence; Art; Law; Anthropology; Philosophy; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0008827182,0.0002414272,0.0002498454,0.001286708,0.001882765,0.001579662,0.0002392282,0.0002164243,0.002802434],"category_scores_gemma":[0.001827281,0.0001224284,0.0002214649,0.00168464,0.0009979707,0.0008105132,0.001464563,0.0004637409,0.000359689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009173908,"about_ca_system_score_gemma":0.0004893377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01277568,"about_ca_topic_score_gemma":0.02975265,"domain_scores_codex":[0.9994668,0.0001387597,0.00005867856,0.00007556208,0.00008938423,0.000170857],"domain_scores_gemma":[0.9984795,0.0003744353,0.0002331049,0.0001388637,0.0004531644,0.0003210075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00056423,0.00039678,0.7153481,0.0001383701,0.00009581099,0.00161643,0.246559,0.00007784172,0.003870562,0.00228902,0.00082381,0.0282201],"study_design_scores_gemma":[0.00001007773,0.0002012055,0.5945017,0.00005868944,0.00003719849,0.00075086,0.396148,0.0001272065,0.0007862797,0.0001924656,0.007157738,0.00002850633],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997458,0.00004467668,0.00002696087,0.00001934885,0.000006947879,0.000004735557,0.00002355189,3.858212e-7,0.00241539],"genre_scores_gemma":[0.998692,0.00008945226,0.00007843129,0.00003374176,0.000002338223,0.000008389797,0.00008488989,0.000001422957,0.001009379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01277568,"threshold_uncertainty_score":0.02540267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04920574529156543,"score_gpt":0.3342148265888574,"score_spread":0.285009081297292,"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."}}