{"id":"W4413585347","doi":"10.64628/aam.fvhvmpfyp","title":"Settlement services need to improve their online offerings for tech-savvy newcomers","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Settlement (finance); Business; Marketing; Advertising; Finance","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003232337,0.0003936756,0.0003872409,0.001322843,0.004259943,0.00828624,0.001556536,0.002624309,0.1784736],"category_scores_gemma":[0.01569211,0.000225788,0.00062671,0.001964446,0.001001511,0.006168731,0.003470524,0.002371542,0.02824738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001355361,"about_ca_system_score_gemma":0.005460639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006237829,"about_ca_topic_score_gemma":0.01259213,"domain_scores_codex":[0.9982833,0.0006103901,0.00005753174,0.0001072799,0.0003955126,0.000546],"domain_scores_gemma":[0.9868289,0.003374018,0.0009679996,0.0007434854,0.001917621,0.006167959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003715307,0.002572373,0.07755379,0.00058068,0.00005383532,0.001143484,0.01467389,0.0006304759,0.004365492,0.03860236,0.2836829,0.5757694],"study_design_scores_gemma":[0.0002468423,0.0006502814,0.1140337,0.0006367618,0.00007737096,0.0008464453,0.1049204,0.002889897,0.001306294,0.04615415,0.7281089,0.0001290135],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.5478116,0.00195691,0.01345223,0.1472079,0.003827716,0.0007575109,0.001453841,0.002068808,0.2814634],"genre_scores_gemma":[0.7319573,0.002320595,0.01563264,0.01954,0.002009783,0.0005387088,0.001547535,0.0008723942,0.225581],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.1784736,"threshold_uncertainty_score":0.5970536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372283927670548,"score_gpt":0.327059068974846,"score_spread":0.3033362296981405,"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."}}