{"id":"W4387536765","doi":"10.1007/s12134-023-01076-y","title":"The Differential Inclusion of Migrant Farmworkers’ and the Landscape of Support in a Migrant-intensive Region in Ontario, Canada","year":2023,"lang":"en","type":"article","venue":"Journal of International Migration and Integration / Revue de l integration et de la migration internationale","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Mitacs","keywords":"Inclusion (mineral); Differential (mechanical device); Migrant workers; Geography; Socioeconomics; Sociology; Gender studies; 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":[0.0005713459,0.000195246,0.0004134599,0.001628864,0.007004141,0.003046847,0.001658112,0.0006078578,0.003628723],"category_scores_gemma":[0.002216335,0.0003222395,0.0003381195,0.004198784,0.002167047,0.000730908,0.002441427,0.0007614651,0.0002129174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03068993,"about_ca_system_score_gemma":0.04737293,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9960586,"about_ca_topic_score_gemma":0.9992247,"domain_scores_codex":[0.9990609,0.00009824355,0.00003812603,0.00009799297,0.0001630243,0.0005416549],"domain_scores_gemma":[0.9975465,0.0001824047,0.000425153,0.00006166299,0.0008299107,0.0009543609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002048321,0.00007498178,0.901116,0.0001060339,0.00006286688,0.0006520024,0.08135288,0.0001700432,0.0007567214,0.001431624,0.003110238,0.01096175],"study_design_scores_gemma":[0.000006342857,0.00001346378,0.913771,0.00008633079,0.00001487283,0.00005950189,0.08329006,0.00009477748,0.00004309323,0.0000671588,0.002539101,0.00001439763],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966602,0.0002216518,0.00002899929,0.0005063526,0.000007166746,0.00001416205,0.0003749983,0.000002201062,0.002184291],"genre_scores_gemma":[0.9976963,0.0002159303,0.00005782874,0.00007443856,0.000003015644,0.00001266488,0.0001598993,0.000002868963,0.001776956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03068993,"threshold_uncertainty_score":0.222672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01217326521329301,"score_gpt":0.2712397841890539,"score_spread":0.2590665189757609,"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."}}