{"id":"W4252488977","doi":"10.32920/ryerson.14668080","title":"The place for immigrants in Toronto's transit and transportation city","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Public transport; Immigration; Demographics; Limiting; Transit (satellite); Work (physics); Public policy; Economic Justice; Sociology; Geography; Regional science; Business; Political science; Public relations; Transport engineering; Economic growth; Engineering; Economics","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":[0.000943567,0.0003839405,0.0003209334,0.00055962,0.01795437,0.004187428,0.0007882242,0.001133927,0.005387498],"category_scores_gemma":[0.001664818,0.0002258268,0.0002903595,0.001431681,0.0106869,0.00164937,0.004464167,0.001376098,0.0001951364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02082804,"about_ca_system_score_gemma":0.01688584,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7404897,"about_ca_topic_score_gemma":0.8838822,"domain_scores_codex":[0.9986828,0.0006033283,0.00002731658,0.00007092486,0.0001195499,0.0004961341],"domain_scores_gemma":[0.9988263,0.0002338712,0.0001422936,0.00003319264,0.00013826,0.0006260233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003172997,0.000005338607,0.006737481,0.00006168098,0.000005391213,0.001235675,0.9824752,0.00006469841,0.0004285451,0.004288844,0.002079379,0.002586149],"study_design_scores_gemma":[0.000001302214,0.000009925166,0.003363396,0.00004480811,0.000004808096,0.00008546441,0.9841588,0.00002095774,0.00003320311,0.0001147731,0.01215477,0.000007877697],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744359,0.00123328,0.000369862,0.004520435,0.000121228,0.00002581815,0.0001173329,0.0000159177,0.01916024],"genre_scores_gemma":[0.9950772,0.0004546101,0.0001196101,0.0003143981,0.00001226551,0.00001311653,0.00002962166,0.000008591326,0.003970657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2595103,"threshold_uncertainty_score":0.5220773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02533389994971528,"score_gpt":0.3225678402310148,"score_spread":0.2972339402812995,"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."}}