{"id":"W7099955771","doi":"","title":"THE RESPONSE OF THE MUNCIPAL PUBLIC SERVICE SECTOR TO THE CHALLENGE OF IMMIGRANT SETTLEMENT","year":2000,"lang":"en","type":"article","venue":"","topic":"Pasture and Agricultural Systems","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Citizenship; Settlement (finance); Public sector; Public service; Service (business)","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":[0.004765741,0.0001673571,0.0002052144,0.0005392501,0.01283991,0.005521106,0.001132953,0.005531376,0.02327883],"category_scores_gemma":[0.007955455,0.0001514818,0.0002174366,0.0007840594,0.005554585,0.001568714,0.006389504,0.003915556,0.0009530063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01644934,"about_ca_system_score_gemma":0.05176046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3707771,"about_ca_topic_score_gemma":0.6052595,"domain_scores_codex":[0.9959055,0.00109214,0.00004200953,0.00008266966,0.0004139211,0.002463777],"domain_scores_gemma":[0.9903806,0.00147635,0.0005397513,0.0001804215,0.001379774,0.006042996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0004258488,0.0003065735,0.06591339,0.0003603185,0.00003320076,0.005463695,0.1988241,0.0004370011,0.00212103,0.1657461,0.4218507,0.1385181],"study_design_scores_gemma":[0.00004740808,0.000143938,0.04502123,0.0003869983,0.000007326112,0.0005065451,0.4103184,0.0004355269,0.0001439108,0.007335932,0.5356203,0.00003250715],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2607943,0.001898913,0.0002702647,0.6103286,0.001533594,0.00008298685,0.0001412756,0.00002229598,0.1249279],"genre_scores_gemma":[0.901145,0.001986762,0.0002323054,0.03767381,0.0004693154,0.00005760983,0.00007429104,0.00002240485,0.05833845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3707771,"threshold_uncertainty_score":0.7372379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0270020752732754,"score_gpt":0.2085617304217165,"score_spread":0.1815596551484411,"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."}}