{"id":"W7099460706","doi":"","title":"Migration Industry Surrounding Temporary Agricultural Migration in Canada","year":2016,"lang":"en","type":"article","venue":"","topic":"Emotional Intelligence and Performance","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Ethnography; Agriculture; Migrant workers; Resource (disambiguation); Human migration","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003576749,0.0001508207,0.0001379432,0.0007814074,0.01146042,0.001742461,0.0007388829,0.0003758982,0.003538987],"category_scores_gemma":[0.001049393,0.0001209095,0.0001568034,0.001598136,0.001919373,0.0003948356,0.002166426,0.0007936229,0.0001297991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0322003,"about_ca_system_score_gemma":0.06784429,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9893058,"about_ca_topic_score_gemma":0.997092,"domain_scores_codex":[0.9995205,0.00004302057,0.000008519622,0.0000378375,0.00007553054,0.0003144325],"domain_scores_gemma":[0.9991093,0.00005541725,0.0001092717,0.00001720585,0.0003041631,0.0004045999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003881343,0.0002488456,0.5088599,0.0002500648,0.00003548324,0.007099729,0.3692403,0.0004049862,0.004139681,0.008810633,0.01785886,0.08266342],"study_design_scores_gemma":[0.000006323315,0.00004399745,0.4596162,0.0001043565,0.0000138911,0.0003218294,0.4931938,0.0001996829,0.0003218914,0.0001903388,0.04596096,0.00002683353],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863039,0.0002127125,0.00005829584,0.001313237,0.00002359745,0.0000285939,0.0002080045,0.000007450078,0.01184423],"genre_scores_gemma":[0.9933839,0.0003856058,0.000105164,0.0001761928,0.000005974182,0.000009102539,0.0001159843,0.00000382445,0.005814334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0322003,"threshold_uncertainty_score":0.2336305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04257998584987796,"score_gpt":0.2954601258816813,"score_spread":0.2528801400318034,"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."}}