{"id":"W2976553957","doi":"10.1177/0038026119878939","title":"The locative imaginary: Classification, context and relevance in location analytics","year":2019,"lang":"en","type":"article","venue":"The Sociological Review","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Relevance (law); Analytics; Credibility; Sociology; Data science; The Imaginary; Context (archaeology); Social media analytics; Politics; Social media; Computer science; Epistemology; Political science; Psychology; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01585549,0.0008003009,0.001189171,0.01099415,0.002679544,0.01476151,0.001618725,0.003290167,0.002797468],"category_scores_gemma":[0.04018152,0.0004581506,0.0006545116,0.01165659,0.02358636,0.02613985,0.005423333,0.003981377,0.000707491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004774497,"about_ca_system_score_gemma":0.005022034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003828399,"about_ca_topic_score_gemma":0.005410153,"domain_scores_codex":[0.9820718,0.01278238,0.0007586771,0.001032011,0.002980873,0.0003742254],"domain_scores_gemma":[0.9410185,0.04887646,0.002553321,0.002344377,0.00462749,0.0005799063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003286698,0.00002469768,0.003639605,0.003397701,0.00008254204,0.0001811049,0.01219598,0.0008398522,0.0002413644,0.8164393,0.01024149,0.1526835],"study_design_scores_gemma":[0.0000115001,0.00006932757,0.006053985,0.00871239,0.00009317495,0.0006708411,0.0248356,0.002218915,0.0004174717,0.5278336,0.4289904,0.00009290166],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02164032,0.637553,0.1013766,0.1460972,0.004779086,0.0001244812,0.0003271165,0.000134619,0.08796752],"genre_scores_gemma":[0.5353171,0.4068855,0.03237546,0.009929406,0.009503755,0.0002608732,0.0003111862,0.0001599031,0.005256825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01585549,"threshold_uncertainty_score":0.08385283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05189392038088392,"score_gpt":0.3637051557333565,"score_spread":0.3118112353524726,"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."}}