{"id":"W2751778071","doi":"10.1177/1745691617708630","title":"Constraints on Generality (COG): A Proposed Addition to All Empirical Papers","year":2017,"lang":"en","type":"article","venue":"Perspectives on Psychological Science","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":987,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"University of Illinois at Urbana-Champaign","keywords":"Generality; Cog; Computer science; Empirical research; Psychology; Programming language; Cognitive science; Artificial intelligence; Mathematics; Statistics","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","scholarly_communication","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.004207874,0.0002619311,0.0003679203,0.0003097791,0.001368626,0.001653426,0.003458711,0.0001401054,0.002998634],"category_scores_gemma":[0.01346492,0.0001613689,0.0001945817,0.0005594496,0.003273639,0.0004625099,0.0003174852,0.0003628942,0.002772258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002653538,"about_ca_system_score_gemma":0.00007404015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001188798,"about_ca_topic_score_gemma":0.00001196623,"domain_scores_codex":[0.9944537,0.0001769099,0.0005154632,0.002407024,0.001826758,0.0006200905],"domain_scores_gemma":[0.9959424,0.0006117315,0.0003426896,0.002139799,0.0003552616,0.0006080832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005037088,0.001477465,0.002239332,3.487091e-7,0.000006487783,0.00006325493,0.00141122,0.0001035296,0.007014561,0.008756673,0.01225412,0.9661693],"study_design_scores_gemma":[0.0009701944,0.003150751,0.8929378,0.00004053851,0.00001067782,0.00003583129,0.003645456,0.0002499299,0.0006124408,0.08172671,0.01591821,0.0007015044],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8647593,0.0000035086,0.0001969994,0.01041469,0.0009034071,0.0003634135,0.00005929459,0.00006845665,0.123231],"genre_scores_gemma":[0.9927462,0.000005546633,0.002173069,0.004576932,0.0001357522,0.00002073975,8.547177e-7,0.000007514778,0.0003334131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9654678,"threshold_uncertainty_score":0.9999315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3898253117507056,"score_gpt":0.564905420205657,"score_spread":0.1750801084549514,"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."}}