{"id":"W4416240684","doi":"10.2139/ssrn.5724122","title":"An Investigation of Brain Drain/Gain in Atlantic Canada (2011-21) Using a Revised Index and Probit Model","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Index (typography); Productivity; Probit model; Census; Affect (linguistics); Stock (firearms); Estimation","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":[0.00154987,0.0003509847,0.0005485229,0.00133305,0.001616145,0.00366889,0.001269341,0.0008522941,0.003080596],"category_scores_gemma":[0.007704705,0.0002459646,0.0005557828,0.003884217,0.0009845505,0.0008502132,0.001153219,0.00177619,0.0002199087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0436327,"about_ca_system_score_gemma":0.04512883,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9937496,"about_ca_topic_score_gemma":0.9939225,"domain_scores_codex":[0.99918,0.0001064171,0.00003617437,0.00009424512,0.0002071702,0.0003759368],"domain_scores_gemma":[0.9945938,0.001991782,0.0007517853,0.0001451879,0.001751694,0.0007656865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004516217,0.0002243472,0.8741255,0.0001546975,0.0003599636,0.000606208,0.001399394,0.05004543,0.0003720175,0.03199317,0.01923172,0.02103589],"study_design_scores_gemma":[0.00007297763,0.00014772,0.8147085,0.00009876114,0.0002928746,0.0001109188,0.006309328,0.1526051,0.000768919,0.005592152,0.01916378,0.0001289059],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771363,0.0007600714,0.001141565,0.003703178,0.00004197907,0.00005153527,0.01005132,0.00007507461,0.007039092],"genre_scores_gemma":[0.9915106,0.0004016695,0.0004548225,0.0001260357,0.00001614298,0.00001468651,0.002984968,0.0000118806,0.004479155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0436327,"threshold_uncertainty_score":0.3165789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564849646158731,"score_gpt":0.2786303088507217,"score_spread":0.2629818123891344,"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."}}