{"id":"W2895650968","doi":"10.1017/ssh.2020.16","title":"Military Technology and Sample Selection Bias","year":2020,"lang":"en","type":"preprint","venue":"Social Science History","topic":"Defense, Military, and Policy Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Selection bias; Economics; Bureaucracy; Robustness (evolution); Business cycle; Sample (material); Supply and demand; Labour supply; Labour economics; Demographic economics; Political science; Macroeconomics; Politics; Law","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":[0.000578761,0.0001877123,0.0004532258,0.0006884161,0.0005489423,0.00001454689,0.0003767612,0.0002617469,0.00008270067],"category_scores_gemma":[0.000815441,0.0002422485,0.0001019135,0.0005841794,0.001900186,0.0001085961,0.0005151866,0.0004597557,0.00009609917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230789,"about_ca_system_score_gemma":0.0002415279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004073604,"about_ca_topic_score_gemma":0.0003309672,"domain_scores_codex":[0.9984219,0.00001449075,0.0003561678,0.0007848961,0.00006866895,0.0003538654],"domain_scores_gemma":[0.9994513,0.00004461654,0.0001871164,0.0001746698,0.00005688139,0.00008540835],"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":[0.00002447239,0.00008530924,0.06797766,0.0002810881,0.00009841291,0.000004166843,0.03709027,0.00000885307,0.00009045815,0.7723476,0.1133296,0.00866208],"study_design_scores_gemma":[0.0001179984,0.00005243036,0.03847262,0.000008571209,0.000009999038,0.000001237134,0.0004806698,0.0001684638,0.00000453745,0.3788581,0.5814546,0.000370804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4644578,0.171583,0.001057564,0.02367767,0.01232237,0.001101994,0.001145824,0.000789726,0.3238641],"genre_scores_gemma":[0.9956145,0.0009824391,0.001056547,0.0008664892,0.0005672699,0.00004419359,0.000007362821,0.00001945013,0.00084172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5311568,"threshold_uncertainty_score":0.9878606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1119006488590395,"score_gpt":0.260970304411122,"score_spread":0.1490696555520825,"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."}}