{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.02126255,0.0001466949,0.0005978785,0.0001914996,0.0005492408,0.0001624658,0.001169104,0.00002025204,0.0002663361],"category_scores_gemma":[0.02033592,0.0001143921,0.0001470976,0.006030277,0.01196241,0.0004660414,0.0001985767,0.0002264164,0.0003828177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008376343,"about_ca_system_score_gemma":0.002317576,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007671276,"about_ca_topic_score_gemma":0.00007257699,"domain_scores_codex":[0.9914126,0.004122993,0.0003860901,0.0005639452,0.00179723,0.001717108],"domain_scores_gemma":[0.9953958,0.0008788594,0.0001346416,0.000406935,0.0008175102,0.00236623],"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":[0.000001328005,0.00005574208,0.0002626902,0.00005307357,0.000009619167,0.000001384066,0.00181236,3.519852e-7,0.00003484552,0.9306347,0.002401967,0.06473192],"study_design_scores_gemma":[0.0001819699,0.0002189849,0.001042765,0.0005621462,0.0001030827,0.000006827233,0.04662689,0.001512578,0.0001578747,0.07581668,0.8730333,0.0007369191],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0003906215,0.0107007,0.1819742,0.02823651,0.00006391981,0.0006599607,0.000002852907,0.00009966796,0.7778716],"genre_scores_gemma":[0.7469438,0.004410333,0.2350152,0.0113981,0.0003180187,0.0001574722,0.000003252131,0.00002134507,0.001732432],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8706313,"threshold_uncertainty_score":0.9989367,"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."}}