{"id":"W2004107264","doi":"10.1111/j.1541-1338.2006.00209.x","title":"How Big Is a Policy Network? An Assessment Utilizing Data From Canadian Royal Commissions 1970–2000","year":2006,"lang":"en","type":"article","venue":"Review of Policy Research","topic":"Social Policy and Reform Studies","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Operationalization; Consistency (knowledge bases); Metaphor; Work (physics); Replication (statistics); Estimation; Computer science; Big data; Management science; Sociology; Operations research; Political science; Data science; Epistemology; Economics; Management; Data mining; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008806358,0.0002781794,0.0006465698,0.01223709,0.004536782,0.005351613,0.001664282,0.0007181434,0.003716133],"category_scores_gemma":[0.05655068,0.0003333531,0.0003081559,0.036686,0.002597317,0.002975505,0.00209542,0.0009741371,0.0002385806],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09388577,"about_ca_system_score_gemma":0.05235408,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9912406,"about_ca_topic_score_gemma":0.9945549,"domain_scores_codex":[0.9940987,0.0009796011,0.0002942889,0.0005437045,0.003226008,0.0008578328],"domain_scores_gemma":[0.9545716,0.01289755,0.005962815,0.001612193,0.02185607,0.00309985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001678541,0.00006224977,0.799332,0.0004879938,0.0002100455,0.0002720821,0.01771702,0.0167927,0.0002320265,0.06975792,0.02521976,0.06974838],"study_design_scores_gemma":[0.000009990381,0.00002801353,0.934787,0.0002250759,0.00009382325,0.00005709879,0.01519038,0.01121887,0.0002301722,0.003764896,0.03433478,0.00005977617],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9068869,0.002980583,0.002750317,0.005937161,0.00003619428,0.0002431539,0.0239075,0.000085397,0.05717274],"genre_scores_gemma":[0.9920339,0.001192495,0.00111686,0.00007928409,0.000009665441,0.00004805802,0.003460259,0.0000143045,0.002045223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9954632,"threshold_uncertainty_score":0.6811922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3425485529370437,"score_gpt":0.5675117602292045,"score_spread":0.2249632072921607,"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."}}