{"id":"W4386805108","doi":"10.1016/j.tranpol.2023.09.012","title":"Why do planners do what they do? and what are the implications? Guidance from on-demand ride-hailing policy in Toronto and Vancouver, Canada","year":2023,"lang":"en","type":"article","venue":"Transport Policy","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Anticipation (artificial intelligence); Transportation planning; Thematic analysis; Marketing; Demand patterns; Economics; Business; Demand management; Sociology; Transport engineering; Qualitative research; Engineering; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01060838,0.0007982213,0.001002839,0.002596338,0.01275651,0.0173461,0.00225158,0.005026253,0.01218116],"category_scores_gemma":[0.04730756,0.001134134,0.0005548142,0.007233723,0.006936746,0.005696085,0.00301968,0.006120051,0.001112296],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1766074,"about_ca_system_score_gemma":0.4449571,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.99488,"about_ca_topic_score_gemma":0.9980528,"domain_scores_codex":[0.9854372,0.004250374,0.0003915863,0.0005993022,0.002584792,0.006736883],"domain_scores_gemma":[0.964956,0.009994043,0.001241234,0.0004849443,0.01593451,0.007389294],"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":[0.0002981206,0.0002692228,0.0752366,0.001373187,0.0001262336,0.0007195571,0.05783208,0.02593469,0.0004653452,0.1982861,0.5205342,0.1189247],"study_design_scores_gemma":[0.0003800346,0.00009943063,0.131478,0.004191475,0.0001995373,0.0001062656,0.2871646,0.01413198,0.001079613,0.05962314,0.5010585,0.0004873754],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1692722,0.01048427,0.005364349,0.5806117,0.000737341,0.0005689457,0.002842165,0.0002508107,0.2298683],"genre_scores_gemma":[0.9266697,0.01062346,0.005262379,0.01349177,0.0001128288,0.0002858955,0.001056212,0.0001342606,0.04236349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1766074,"threshold_uncertainty_score":0.9550187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01064775281621158,"score_gpt":0.2526704929588324,"score_spread":0.2420227401426208,"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."}}