{"id":"W4321352052","doi":"10.1111/beer.12524","title":"Board gender diversity, government subsidies, and green vehicles sales: Evidence from China","year":2023,"lang":"en","type":"article","venue":"Business Ethics the Environment & Responsibility","topic":"Gender Diversity and Inequality","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Higher Education Discipline Innovation Project; National Office for Philosophy and Social Sciences","keywords":"Subsidy; Corporate governance; Diversity (politics); Government (linguistics); China; Business; Gender diversity; Sustainability; Representation (politics); Economics; Market economy; Finance; Politics","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":[0.001247057,0.0002818918,0.0003143037,0.001483026,0.0008421663,0.0007515731,0.0004442328,0.0003030878,0.003120593],"category_scores_gemma":[0.002564552,0.0001442505,0.0005271391,0.001983772,0.001059302,0.0005333821,0.0009487786,0.000464547,0.0001503063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0013123,"about_ca_system_score_gemma":0.002029778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1022461,"about_ca_topic_score_gemma":0.1414158,"domain_scores_codex":[0.9993252,0.0001464969,0.00003935435,0.0001110622,0.0001693207,0.0002086653],"domain_scores_gemma":[0.9960114,0.000837483,0.00190409,0.0001693167,0.0005145014,0.0005631672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004748775,0.00007630314,0.9920111,0.00004180668,0.00007191962,0.000129278,0.0007559177,0.0001452806,0.00007090881,0.0005115868,0.0004942603,0.005644091],"study_design_scores_gemma":[0.000007290299,0.00004700948,0.9971566,0.00003200994,0.00004969151,0.00001780014,0.001340017,0.0003358156,0.00008400535,0.0001191625,0.0008043779,0.000006149337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981722,0.0003036066,0.00005741369,0.0002031819,0.000006818997,0.000004886907,0.0001860072,0.000001640735,0.001064269],"genre_scores_gemma":[0.9993673,0.0001505094,0.00001834705,0.00003374091,0.000006226946,0.000002915454,0.0001557917,5.873541e-7,0.0002645885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1022461,"threshold_uncertainty_score":0.2033019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2226892570537411,"score_gpt":0.3157074393719973,"score_spread":0.09301818231825618,"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."}}