{"id":"W2175882902","doi":"10.1080/02723638.2015.1050922","title":"Dazzled by data: Big Data, the census and urban geography","year":2015,"lang":"en","type":"article","venue":"Urban Geography","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Census; Geography; Big data; Urban geography; Regional science; Data science; Economic geography; Sociology; Demography; Urban planning; Computer science; Biology; Population; Data mining; Ecology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.02647032,0.001323526,0.001974523,0.006956923,0.003553117,0.01548294,0.002757015,0.008034951,0.0072393],"category_scores_gemma":[0.1293054,0.0009573236,0.001393524,0.01063307,0.01008525,0.0131334,0.005166586,0.02354752,0.003116869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004646907,"about_ca_system_score_gemma":0.007751545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008646876,"about_ca_topic_score_gemma":0.01738953,"domain_scores_codex":[0.9758123,0.0119449,0.00264998,0.001928055,0.007130188,0.0005345737],"domain_scores_gemma":[0.8156902,0.1418792,0.007317595,0.005247705,0.0235087,0.006356545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002095917,0.000002953746,0.0002169055,0.0004870408,0.00004398432,0.00003424819,0.0003023763,0.00003967245,0.00003172567,0.01429915,0.9677664,0.01675454],"study_design_scores_gemma":[0.00001315638,0.000006732872,0.0004857271,0.001684016,0.0000228659,0.00006777133,0.0003832419,0.00009741338,0.00003764654,0.01272605,0.9844417,0.00003357324],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0001872893,0.07397206,0.002227275,0.5831231,0.3369204,0.00002321652,0.0009461461,0.0001361311,0.002464445],"genre_scores_gemma":[0.00617271,0.09505251,0.006057024,0.3489208,0.5319958,0.0001299828,0.001539363,0.0007568106,0.009375005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9964469,"threshold_uncertainty_score":0.1399901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08380708350718923,"score_gpt":0.3028242865708041,"score_spread":0.2190172030636148,"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."}}