{"id":"W2077943567","doi":"10.1007/s12134-015-0425-1","title":"Knowledge Mobilization/Transfer and Immigration Policy: Forging Space for NGOs—the Case of CERIS—The Ontario Metropolis Centre","year":2015,"lang":"en","type":"article","venue":"Journal of International Migration and Integration / Revue de l integration et de la migration internationale","topic":"Evaluation and Performance Assessment","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; York University; Toronto Metropolitan University","funders":"","keywords":"Immigration; Knowledge transfer; Settlement (finance); Government (linguistics); Political science; Public relations; Public administration; Immigration policy; Economic growth; Sociology; Business; Economics; Management","routes":{"ca_aff":true,"ca_fund":false,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002446272,0.0002623834,0.0002742721,0.000697343,0.02609092,0.00894379,0.001806721,0.003730083,0.007798795],"category_scores_gemma":[0.004523078,0.0002841853,0.0003308062,0.001388957,0.01318344,0.002430929,0.004993632,0.002413919,0.0002835607],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07724406,"about_ca_system_score_gemma":0.1144975,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9547808,"about_ca_topic_score_gemma":0.989523,"domain_scores_codex":[0.9965424,0.0007415507,0.00004421766,0.0001306549,0.0003660597,0.002175166],"domain_scores_gemma":[0.9962638,0.001108293,0.0002404452,0.0001563651,0.0005636063,0.001667372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005924163,0.0004350375,0.1014852,0.0005130318,0.0001419618,0.02057374,0.3248849,0.005194653,0.002653716,0.4322315,0.04272898,0.06856488],"study_design_scores_gemma":[0.000129865,0.0001284309,0.07968683,0.0004705201,0.00008352959,0.0008916975,0.6513487,0.002251207,0.0008970296,0.01694975,0.2470742,0.00008833798],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7753754,0.0009087945,0.0006538595,0.04040812,0.00008745758,0.0002730417,0.00008464771,0.00001547034,0.1821932],"genre_scores_gemma":[0.9753875,0.0003499648,0.000348891,0.0008734727,0.00001797047,0.00003955319,0.00001656931,0.000006636442,0.02295945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9910562,"threshold_uncertainty_score":0.5604475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08883022023495073,"score_gpt":0.4355564453671454,"score_spread":0.3467262251321946,"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."}}