{"id":"W7048053611","doi":"","title":"Interview with David Prendergast on âMediating Between Technology and People in Smart City Transformationsâ","year":2018,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Lightning and Electromagnetic Phenomena","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; University College London; Imperial College London","keywords":"Indigenous; Smart city; Phenomenon; Social media; Social relationship","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003418741,0.0007439695,0.0006038101,0.001132644,0.01828456,0.004871197,0.001233295,0.008139178,0.0149886],"category_scores_gemma":[0.0110978,0.0006858756,0.000445422,0.001441362,0.006211309,0.009608426,0.005112211,0.01828331,0.002059826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006448004,"about_ca_system_score_gemma":0.004589224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08327312,"about_ca_topic_score_gemma":0.1504436,"domain_scores_codex":[0.9965093,0.002162163,0.0001020203,0.000258716,0.0004866854,0.0004810884],"domain_scores_gemma":[0.9955181,0.002163821,0.0002265627,0.00008047999,0.0007072389,0.001303811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000371135,0.0000567315,0.001803394,0.00011465,0.00000831417,0.001862452,0.3293968,0.00006650851,0.000403859,0.008300422,0.647242,0.01070768],"study_design_scores_gemma":[0.000005877414,0.00002332891,0.00109293,0.0001798802,0.000002898111,0.0005876852,0.2634121,0.00003406256,0.00008968772,0.0008584571,0.7336782,0.00003488773],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03889512,0.01104752,0.0009541968,0.8879589,0.009310139,0.0001462644,0.0005356059,0.00006864205,0.05108369],"genre_scores_gemma":[0.2250684,0.009006176,0.001061315,0.6165447,0.00171429,0.000431629,0.0001995729,0.0001737765,0.1458002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08327312,"threshold_uncertainty_score":0.1655768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008604652894020978,"score_gpt":0.2207373135322092,"score_spread":0.2121326606381882,"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."}}