{"id":"W2844137832","doi":"10.3138/9781442667938-016","title":"11 Small Cities as Talent Accelerators: Talent Mobility and Knowledge Flows in Moncton","year":2014,"lang":"en","type":"book-chapter","venue":"University of Toronto Press eBooks","topic":"Migration, Aging, and Tourism Studies","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"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":[0.0003142006,0.0003621079,0.0002183588,0.001176031,0.003675974,0.006997428,0.0006999579,0.001068715,0.02459886],"category_scores_gemma":[0.0005245258,0.0001803785,0.0002093403,0.005297003,0.003274382,0.002715829,0.001981972,0.001157758,0.001063917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.015097,"about_ca_system_score_gemma":0.01495191,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3421015,"about_ca_topic_score_gemma":0.6808447,"domain_scores_codex":[0.9998017,0.00005928466,0.000003626555,0.00002114692,0.00002827783,0.00008607624],"domain_scores_gemma":[0.9997573,0.00008693723,0.0000219543,0.00001038271,0.00003806379,0.00008540838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003481672,0.0000340433,0.003110903,0.0001792587,0.000006070063,0.000234575,0.02799604,0.0005774534,0.000103242,0.7980551,0.1046882,0.06498019],"study_design_scores_gemma":[0.00001454905,0.00001967254,0.01377169,0.0004914918,0.00001943548,0.0001214079,0.03691057,0.0007231524,0.0001470165,0.08162389,0.8661327,0.00002432123],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04636896,0.04460777,0.001185194,0.04280844,0.0007562819,0.00004435849,0.0004983976,0.00007254557,0.863658],"genre_scores_gemma":[0.4844103,0.02690031,0.0008758368,0.001914367,0.000298129,0.00005972645,0.0002499289,0.00006168659,0.4852297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6578985,"threshold_uncertainty_score":0.6802205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02728551207610149,"score_gpt":0.2376664110272715,"score_spread":0.21038089895117,"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."}}