{"id":"W4405946688","doi":"10.1186/s40878-024-00414-y","title":"The hidden power of provincial and territorial immigration programs in shaping Canada’s immigration landscape","year":2024,"lang":"en","type":"article","venue":"Comparative Migration Studies","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Immigration; Power (physics); Economic geography; Political science; Geography; Regional science; Economic growth; Development economics; Economics; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000650847,0.0001379828,0.000226643,0.00008714359,0.0005355111,0.0001990358,0.00009766234,0.00006196777,0.00000600519],"category_scores_gemma":[0.0001323017,0.00009887438,0.00003698198,0.0004223774,0.0002774,0.0003036732,0.00002891173,0.0001385499,0.000001547709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002045483,"about_ca_system_score_gemma":0.0007338042,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1482886,"about_ca_topic_score_gemma":0.9973062,"domain_scores_codex":[0.99848,0.0002816311,0.0004064162,0.0002265431,0.0004099691,0.000195439],"domain_scores_gemma":[0.9990934,0.0003484186,0.0001182557,0.00008678636,0.0003117207,0.00004146214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001207919,0.00007019225,0.02724124,0.00007390353,0.0001832525,0.00000496022,0.7929392,0.00004500842,0.0005270567,0.1312881,0.03219571,0.01531062],"study_design_scores_gemma":[0.0005128917,0.000216467,0.01752874,0.0002808026,0.00005483,0.000001152202,0.6375763,0.01589424,0.0002528811,0.001832555,0.3254091,0.0004400246],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834194,0.008070251,0.0002060461,0.005492503,0.001348279,0.0006696347,0.00001625056,0.00005159197,0.0007260994],"genre_scores_gemma":[0.9977342,0.001006199,0.00005570031,0.00002756847,0.0003919295,0.00008597054,0.00002509884,0.000005906579,0.0006674597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8490177,"threshold_uncertainty_score":0.857383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0657927412028523,"score_gpt":0.3459714728155177,"score_spread":0.2801787316126654,"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."}}