{"id":"W3176225590","doi":"10.3390/philosophies6020052","title":"Naturalizing Logic: How Knowledge of Mechanisms Enhances Inductive Inference","year":2021,"lang":"en","type":"article","venue":"Philosophies","topic":"Philosophy and History of Science","field":"Arts and Humanities","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inference; Counterfactual thinking; Generalization; Interpretation (philosophy); Inductive reasoning; Analogy; Defeasible estate; Computer science; Causal inference; Mechanism (biology); Artificial intelligence; Cognitive science; Argument (complex analysis); Machine learning; Epistemology; Psychology; Mathematics; Econometrics; Philosophy","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":[0.0001209333,0.0001666977,0.0002708985,0.0001410031,0.0004250393,0.0001015921,0.0002885581,0.00005073441,0.000488733],"category_scores_gemma":[0.0001187728,0.0001438119,0.0001011884,0.0001212815,0.0009271549,0.0006578324,0.0001181082,0.0001813979,0.00005109423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002721679,"about_ca_system_score_gemma":0.00007973339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001753738,"about_ca_topic_score_gemma":0.0002368172,"domain_scores_codex":[0.9990515,0.00005665256,0.0001845372,0.0003052836,0.0001988602,0.0002031689],"domain_scores_gemma":[0.9990601,0.0001093159,0.0001475391,0.0002183685,0.0004075305,0.00005714436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006390588,0.00007339752,0.000002315161,0.00004943733,0.00002103361,0.000005358433,0.02271604,7.20948e-7,0.02415596,0.952213,0.0001723299,0.0005840219],"study_design_scores_gemma":[0.0001122634,0.000100456,0.00002424279,0.00008277334,0.00001879057,0.000003219753,0.004726588,0.000008339033,0.03475985,0.9407192,0.01923398,0.0002103211],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1141515,0.01063507,0.0004395712,0.02310814,0.004774518,0.0002803065,0.000112764,0.0002712471,0.8462269],"genre_scores_gemma":[0.9963338,0.0001089449,0.0007282876,0.0002455862,0.0006760535,0.00001106995,0.000006247853,0.000009330795,0.00188067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8821823,"threshold_uncertainty_score":0.5864478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1137541644858955,"score_gpt":0.2853668328929816,"score_spread":0.1716126684070861,"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."}}