{"id":"W2484659677","doi":"","title":"Assigning Refugees to Landlords in Sweden: Stable Maximum Matchings","year":2016,"lang":"en","type":"article","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Ragnar Söderbergs stiftelse","keywords":"Refugee; Residence; Accommodation; Landlord; Matching (statistics); State (computer science); Demographic economics; Political science; Economics; Mathematics; Law; Psychology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.003128756,0.0005656476,0.001200935,0.001436127,0.002147362,0.00225349,0.001580365,0.001578664,0.006983638],"category_scores_gemma":[0.01433576,0.0005836344,0.0009073612,0.001834769,0.001281336,0.002706399,0.003318251,0.000878918,0.0009571845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00153543,"about_ca_system_score_gemma":0.001792046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005769627,"about_ca_topic_score_gemma":0.006122258,"domain_scores_codex":[0.9978872,0.000992314,0.0001364072,0.0004462416,0.000140686,0.000397064],"domain_scores_gemma":[0.9970978,0.001612874,0.0003544173,0.000333057,0.0002578938,0.0003438417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001966059,0.0006815268,0.04374085,0.0005971075,0.00027448,0.001143154,0.003402821,0.4573245,0.005519338,0.1238514,0.008945976,0.3525527],"study_design_scores_gemma":[0.0002538293,0.0005334195,0.008281255,0.0001367045,0.0001043291,0.0006929048,0.004727618,0.795428,0.00436455,0.1764104,0.008994049,0.00007300683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6771747,0.0003266759,0.3110185,0.001205069,0.00005872106,0.0002941487,0.0004702214,0.0004245958,0.009027355],"genre_scores_gemma":[0.8604798,0.0001358437,0.1355538,0.00009069445,0.00001517668,0.0001172472,0.00044466,0.00006387533,0.003098937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006983638,"threshold_uncertainty_score":0.02336258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007416630069901542,"score_gpt":0.2125247259180354,"score_spread":0.2051080958481339,"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."}}