{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008663104,0.0002124585,0.0002386771,0.00003376781,0.003168709,0.0001117427,0.0007474647,0.0002477226,0.000211907],"category_scores_gemma":[0.002125121,0.0001780603,0.00008909796,0.0003733422,0.001684343,0.0004191667,0.002633024,0.0006032066,0.0002029222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004316641,"about_ca_system_score_gemma":0.0001761891,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03058178,"about_ca_topic_score_gemma":0.003840055,"domain_scores_codex":[0.9942849,0.002364419,0.0002611768,0.0006652988,0.001977575,0.0004465894],"domain_scores_gemma":[0.996474,0.002408643,0.0001448472,0.0007579034,0.00005121185,0.0001633417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003874219,0.0001404866,0.8158953,0.00009606366,0.00008807869,0.00002219134,0.1775078,0.0001698526,0.001091552,0.001822055,0.0007157201,0.002063581],"study_design_scores_gemma":[0.0002003323,0.0000137423,0.9627607,0.00003237961,0.00006730583,1.66478e-7,0.01293105,0.00004745286,0.00008976744,0.0177465,0.005894737,0.0002157875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685544,0.0004929082,0.0002498983,0.02946127,0.0002120689,0.0004128161,0.0001313767,0.0001147601,0.0003705296],"genre_scores_gemma":[0.9933803,0.004798981,0.000113631,0.0003568565,0.0001035477,0.000008346718,0.00001315858,0.00001055573,0.001214599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1645767,"threshold_uncertainty_score":0.998129,"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."}}