{"id":"W4391448834","doi":"10.1080/10971475.2024.2310328","title":"Culture and Economic Development in Late Comers: Comparing China and India","year":2024,"lang":"en","type":"article","venue":"Chinese Economy","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"China; History; Development economics; Economics; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003898595,0.0001122321,0.0001840958,0.000104748,0.0002084646,0.0002533952,0.0000845603,0.00006712863,0.000135357],"category_scores_gemma":[0.00001049557,0.0001026879,0.00001994687,0.00006959071,0.00009883747,0.0005031024,0.00004948383,0.0001192927,0.00006407923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001312064,"about_ca_system_score_gemma":0.0001254318,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001228747,"about_ca_topic_score_gemma":0.02823168,"domain_scores_codex":[0.9993091,0.00003012667,0.0002081732,0.0002507017,0.00002204627,0.0001798247],"domain_scores_gemma":[0.999765,0.00003905653,0.00003342699,0.00006127568,0.000005016464,0.0000962303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002807657,0.000008980098,0.8408792,0.00005504013,0.00002494528,0.000002779793,0.1443543,0.00004108121,0.000002097176,0.01068073,0.0003668534,0.003581232],"study_design_scores_gemma":[0.0003082292,0.000008281339,0.8002519,0.00005450429,0.000005197088,0.000002004804,0.001674539,0.002227396,0.0000091529,0.01084314,0.1843421,0.0002735525],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9331904,0.001655183,0.00001079791,0.0003084298,0.0002032135,0.0001505233,0.000001591053,0.00004082909,0.06443901],"genre_scores_gemma":[0.9978777,0.0004608891,0.0001284882,0.000066475,0.000173449,0.00001875194,0.000008818804,0.000007467276,0.001257988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1839752,"threshold_uncertainty_score":0.9895006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410334597543382,"score_gpt":0.2727325662373417,"score_spread":0.2586292202619078,"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."}}