{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004240731,0.0002098473,0.0002075207,0.002187085,0.001142672,0.001494131,0.0003267253,0.0002123443,0.001983418],"category_scores_gemma":[0.001190046,0.00008526288,0.0003525635,0.002622572,0.001058262,0.000613905,0.00129293,0.0005456273,0.0001609981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001421973,"about_ca_system_score_gemma":0.001366905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07971192,"about_ca_topic_score_gemma":0.08363924,"domain_scores_codex":[0.9996682,0.00005668149,0.00001678643,0.00002842665,0.00005220957,0.0001776396],"domain_scores_gemma":[0.9988508,0.0001392327,0.0003216841,0.00004719037,0.0002242071,0.0004168277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001283834,0.0001004831,0.9642526,0.00005856568,0.0001190371,0.0006853826,0.01780858,0.0001629888,0.0002607281,0.003885469,0.0008105518,0.01172735],"study_design_scores_gemma":[0.000003913354,0.00003882114,0.9836547,0.00003316437,0.00003192825,0.00009859724,0.01403602,0.0001175453,0.00008588813,0.0001902145,0.001701338,0.000007927419],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949011,0.0004333286,0.00001699492,0.0002549028,0.00001021633,0.000003053835,0.00007690594,0.000001299697,0.004302163],"genre_scores_gemma":[0.9992369,0.0002904086,0.00001243605,0.00004086855,0.000008209261,0.000002275277,0.00008091264,0.000001189815,0.0003267232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07971192,"threshold_uncertainty_score":0.1584959,"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."}}