{"id":"W2255458947","doi":"10.1017/s0003055415000453","title":"Mixing Methods: A Bayesian Approach","year":2015,"lang":"en","type":"article","venue":"American Political Science Review","topic":"Qualitative Comparative Analysis Research","field":"Social Sciences","cited_by":199,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Process tracing; Causal inference; Bayesian probability; Computer science; Process (computing); Econometrics; Management science; Bayesian inference; Qualitative property; Psychology; Cognitive psychology; Data science; Machine learning; Artificial intelligence; Politics; Mathematics; Political science; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06886626,0.001968375,0.003003665,0.007002688,0.00210538,0.005662397,0.005353443,0.003528465,0.008055024],"category_scores_gemma":[0.1429397,0.002344028,0.003002888,0.005642377,0.004872057,0.007046459,0.005868075,0.00618773,0.001193914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003591776,"about_ca_system_score_gemma":0.004490786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005233231,"about_ca_topic_score_gemma":0.006348392,"domain_scores_codex":[0.9518924,0.04002348,0.001214711,0.003112569,0.003299914,0.0004568756],"domain_scores_gemma":[0.8496546,0.1353156,0.003501374,0.006353012,0.004330255,0.0008450407],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000037833,0.00006202609,0.00142362,0.0003880885,0.0003510929,0.00007658915,0.0008152885,0.03453847,0.0002186753,0.8836495,0.00197111,0.07646783],"study_design_scores_gemma":[0.00003189191,0.00002974712,0.0003217678,0.0001839563,0.00005760726,0.00005750308,0.00008719815,0.09170555,0.0001539532,0.9001352,0.007199415,0.00003631391],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000558911,0.0004779778,0.9969874,0.0005720893,0.00002929312,0.0001012106,0.0000576208,0.00005060495,0.001164781],"genre_scores_gemma":[0.03810352,0.001113498,0.9573674,0.0003931889,0.000205378,0.0012262,0.000158331,0.00008526962,0.001347336],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9311337,"threshold_uncertainty_score":0.3642039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2958060394346289,"score_gpt":0.6121796969617302,"score_spread":0.3163736575271014,"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."}}