{"id":"W4400444903","doi":"10.5465/amproc.2024.14487symposium","title":"Emerging Insights on Social Class at Micro and Macro Levels","year":2024,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"Intergenerational and Educational Inequality Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Queen's University","funders":"","keywords":"Macro; Class (philosophy); Data science; Computer science; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000354685,0.0000881058,0.00009875534,0.0001206907,0.0005724566,0.00006807599,0.0001171054,0.00006106271,0.00008142044],"category_scores_gemma":[0.00002055167,0.00008100166,0.00004152974,0.0001981481,0.0001551689,0.0002159967,0.0001138363,0.00009696498,0.00003360484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001011205,"about_ca_system_score_gemma":0.000009729801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002241231,"about_ca_topic_score_gemma":0.000006310902,"domain_scores_codex":[0.9990742,0.00001079678,0.0001823375,0.000223454,0.0003397136,0.0001695209],"domain_scores_gemma":[0.9998245,0.00004323985,0.00004724501,0.00001027368,0.00004183822,0.00003288232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008721991,0.00001633396,0.0001478345,0.0001637527,0.00008416946,3.369225e-7,0.01927474,3.760431e-7,0.0007474252,0.857704,0.1177454,0.004106881],"study_design_scores_gemma":[0.00007899887,0.00001296999,0.01229217,0.00009374115,0.00003591101,2.973965e-7,0.007120325,0.00001994428,0.001933809,0.02581573,0.9524574,0.0001386762],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8303698,0.0006236936,0.000004025421,0.06887977,0.0002505182,0.0001763462,0.000005767609,0.00005902444,0.09963112],"genre_scores_gemma":[0.9733865,0.000435797,0.0001463805,0.001329618,0.000608089,0.00002591318,0.000001638748,0.00000726792,0.02405875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.834712,"threshold_uncertainty_score":0.4402931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09033961182046385,"score_gpt":0.3801693315427576,"score_spread":0.2898297197222938,"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."}}