{"id":"W2144304780","doi":"10.1177/2053951714535365","title":"Big Data, social physics, and spatial analysis: The early years","year":2014,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Geographer; Epistemology; Big data; Natural (archaeology); Sociology; Social science; History; Geography; Computer science; Economic geography","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":[],"consensus_categories":[],"category_scores_codex":[0.009274307,0.0008081775,0.001050005,0.005794901,0.003821476,0.009750196,0.001049629,0.003573051,0.003314574],"category_scores_gemma":[0.01647813,0.0007320121,0.000563075,0.01187926,0.03357802,0.01732901,0.0050216,0.006461209,0.0004265235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008460767,"about_ca_system_score_gemma":0.004864628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01525383,"about_ca_topic_score_gemma":0.01071166,"domain_scores_codex":[0.9949838,0.003102374,0.0001979293,0.0004748524,0.001025836,0.0002151246],"domain_scores_gemma":[0.9745112,0.02115894,0.0006901002,0.001385326,0.00146962,0.0007848431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003623528,0.00002673933,0.00156282,0.0002976144,0.00002319732,0.00006226978,0.002673633,0.0007245191,0.00004781078,0.9485433,0.007085051,0.03891686],"study_design_scores_gemma":[0.00001060185,0.00001892599,0.002058747,0.0009091724,0.000009568384,0.00008814006,0.002868549,0.00138102,0.0001079021,0.7485232,0.2439905,0.00003360454],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02780244,0.5194578,0.07901835,0.231945,0.005147287,0.00009420454,0.0008563367,0.000151841,0.1355268],"genre_scores_gemma":[0.5852618,0.3197801,0.03926623,0.02760036,0.01500561,0.0003204687,0.0005097142,0.0002281188,0.01202762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01525383,"threshold_uncertainty_score":0.06138742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1568100257423246,"score_gpt":0.2544487952600075,"score_spread":0.09763876951768288,"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."}}