{"id":"W4205213058","doi":"10.7202/1083581ar","title":"The Geographies of Precarious Labour in Canada","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Regional Science","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Geography; Census; Metropolitan area; Spatial ecology; Economic geography; Regional science; Spatial analysis; Range (aeronautics); Scale (ratio); Geographically Weighted Regression; Logistic regression; Cartography; Demography; Sociology; Population; Statistics; Ecology; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005000519,0.0002182231,0.0002944154,0.005375056,0.003327155,0.001674776,0.0008919605,0.0001871882,0.003273958],"category_scores_gemma":[0.003321051,0.0002045777,0.0003665859,0.01214319,0.001355423,0.0004255179,0.001813427,0.0004757395,0.0002252438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03007703,"about_ca_system_score_gemma":0.04175685,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9973888,"about_ca_topic_score_gemma":0.9985389,"domain_scores_codex":[0.9991502,0.00005914486,0.00004860221,0.00009691485,0.0003491902,0.0002958998],"domain_scores_gemma":[0.9974259,0.0002308272,0.0004832303,0.00008491513,0.0012966,0.0004785025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007557892,0.00001532138,0.9307687,0.0002072298,0.00007325709,0.0002588031,0.01126443,0.00141524,0.0003256624,0.006052827,0.01244657,0.03709652],"study_design_scores_gemma":[0.000001802363,0.000004541656,0.9855502,0.0001024335,0.000009016903,0.00005956698,0.006372049,0.0004629672,0.00004242631,0.000265124,0.007113336,0.0000165832],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9491911,0.003485587,0.0007649848,0.002024414,0.00002842513,0.0000722448,0.02555552,0.00005765298,0.01882017],"genre_scores_gemma":[0.9928411,0.0013552,0.0004260318,0.00006237012,0.00000578602,0.00001951019,0.003483317,0.00001025985,0.00179645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03007703,"threshold_uncertainty_score":0.2182251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03371840847185126,"score_gpt":0.3282034241779901,"score_spread":0.2944850157061388,"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."}}