{"id":"W2767356419","doi":"10.3167/trans.2017.070109","title":"Theorizing Mobility Transitions","year":2017,"lang":"en","type":"article","venue":"Transfers","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"Engineering and Physical Sciences Research Council","keywords":"Mobilities; Scholarship; Field (mathematics); Sociology; Perspective (graphical); Multidisciplinary approach; Discipline; State (computer science); Order (exchange); Epistemology; Social science; Computer science; Political science","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.000619242,0.00007711819,0.0001146569,0.00001924763,0.002328709,0.0001549284,0.0004685498,0.00007403103,0.0007156884],"category_scores_gemma":[0.00003336472,0.00007391232,0.0001274255,0.00004659312,0.0008129724,0.0005251264,0.000002916476,0.0001199092,0.00002606306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003239016,"about_ca_system_score_gemma":0.00009786938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002934713,"about_ca_topic_score_gemma":0.01064666,"domain_scores_codex":[0.9991224,0.00005974415,0.0001323146,0.0002097898,0.0002020294,0.0002737057],"domain_scores_gemma":[0.999384,0.00003072284,0.00002099508,0.0004018319,0.00003808876,0.0001243242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001451811,0.0005585128,0.5970969,0.00008343151,0.00008783735,0.00002708806,0.08737678,0.00001049376,0.002635455,0.2546383,0.0002257837,0.05711426],"study_design_scores_gemma":[0.0006905374,0.00004219267,0.9471322,0.00002692414,0.00007308463,1.198744e-7,0.003801568,0.00001285583,0.001091616,0.03354496,0.01325304,0.0003308838],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8354936,0.00002446083,0.002744291,0.004317371,0.0002481734,0.0002072946,0.00002764876,0.0001122026,0.156825],"genre_scores_gemma":[0.9992527,0.00002234898,0.00009749558,0.00006734967,0.0001185929,0.00001174595,0.000003351756,0.00000623697,0.0004201576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3500354,"threshold_uncertainty_score":0.9989702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04005792231243271,"score_gpt":0.3407050436525779,"score_spread":0.3006471213401452,"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."}}