{"id":"W2006730206","doi":"10.1177/00113921030515006","title":"When Methods Make a Difference","year":2003,"lang":"en","type":"article","venue":"Current Sociology","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interdependence; Context (archaeology); Range (aeronautics); Set (abstract data type); Computer science; Epistemology; Sociology; Limiting; Cover (algebra); Data science; Management science; Social science; Economics","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":["metaresearch"],"category_scores_codex":[0.3407791,0.002252576,0.004323641,0.006354649,0.005961713,0.03110079,0.006897897,0.0177662,0.02233061],"category_scores_gemma":[0.6327304,0.00237753,0.003088247,0.005490759,0.04640144,0.04783668,0.01783151,0.0244511,0.01356649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009315138,"about_ca_system_score_gemma":0.01703989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003153162,"about_ca_topic_score_gemma":0.002430819,"domain_scores_codex":[0.5216946,0.3534825,0.02702383,0.02443345,0.06719138,0.006174257],"domain_scores_gemma":[0.3597429,0.4690452,0.01671628,0.09774455,0.05053905,0.00621204],"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":[0.00039531,0.0001934491,0.002845952,0.003289547,0.0008671887,0.0002768266,0.01440803,0.0005373462,0.000541389,0.6955016,0.1025771,0.1785663],"study_design_scores_gemma":[0.0002058713,0.000111375,0.00105237,0.005223844,0.0001972768,0.0001936964,0.005173137,0.001124105,0.0009336322,0.5757974,0.4097993,0.0001880891],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.004203625,0.02818705,0.3413685,0.4634352,0.08211203,0.001610996,0.00112504,0.002182671,0.07577501],"genre_scores_gemma":[0.1069711,0.01403181,0.6410527,0.1730231,0.02698636,0.009200844,0.0009347973,0.003795285,0.02400408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6592209,"threshold_uncertainty_score":0.8129368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1458569409664596,"score_gpt":0.4504666707360928,"score_spread":0.3046097297696332,"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."}}