{"id":"W3125688931","doi":"10.2307/2111095","title":"How Not to Lie with Statistics: Avoiding Common Mistakes in Quantitative
\\nPolitical Science","year":2010,"lang":"en","type":"article","venue":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":502,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Sophistication; Conceptualization; Mathematical proof; Political methodology; Politics; Quality (philosophy); Set (abstract data type); Computer science; Order (exchange); Epistemology; Management science; Positive economics; Data science; Sociology; Political science; Mathematics; Social science; Political philosophy; Law; Artificial intelligence; Engineering; Economics; Systems theory in political science","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":[0.143031,0.001219701,0.002115521,0.004365593,0.006219441,0.01559265,0.005850537,0.009090663,0.002513836],"category_scores_gemma":[0.4377168,0.001194836,0.001053117,0.003693386,0.0852261,0.02940992,0.008159702,0.0209474,0.00191161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004873195,"about_ca_system_score_gemma":0.008253187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00384441,"about_ca_topic_score_gemma":0.003110365,"domain_scores_codex":[0.8480777,0.1056534,0.007815685,0.009182332,0.02788499,0.001385992],"domain_scores_gemma":[0.6137059,0.3042835,0.01399015,0.0419339,0.02353663,0.002549917],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003332253,0.00001870384,0.0007784,0.0002388105,0.00007504394,0.0001853307,0.003985845,0.001416345,0.0001330256,0.9199613,0.0391175,0.03405633],"study_design_scores_gemma":[0.00000942613,0.000009872095,0.0000981376,0.000151724,0.000006597408,0.00007304396,0.0004345182,0.001268076,0.0001354287,0.9786294,0.01915664,0.00002703122],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.005874584,0.01600234,0.3529584,0.5971816,0.01330759,0.00006300964,0.0001146977,0.0005479972,0.01394977],"genre_scores_gemma":[0.3993874,0.02017003,0.3793201,0.1669167,0.02181545,0.0006713014,0.0001372017,0.001759346,0.009822627],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8569689,"threshold_uncertainty_score":0.7564295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08274278832859541,"score_gpt":0.2650841879697026,"score_spread":0.1823413996411072,"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."}}