{"id":"W1587506585","doi":"","title":"Analysis of British Columbia Caseload for Data Persons Working With a Disability and Persons with Persistent and Multiple Barriers","year":2008,"lang":"en","type":"article","venue":"Canadian Review of Social Policy / Revue canadienne de politique sociale","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; Poverty; Government (linguistics); Demographic economics; Christian ministry; Disability benefits; Political science; Social security; Medicine; Economic growth; Business; Economics; Finance; Law","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001776924,0.0001523403,0.0008895188,0.0002560354,0.0007098449,0.00008614665,0.0003979532,0.0001234727,0.00003101299],"category_scores_gemma":[0.002643932,0.000188093,0.0002266612,0.00183518,0.001373926,0.0001516952,0.00003999789,0.0001335452,7.450802e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005049756,"about_ca_system_score_gemma":0.002704876,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9522953,"about_ca_topic_score_gemma":0.9977164,"domain_scores_codex":[0.9977051,0.0002188104,0.0005569556,0.0005907371,0.0002243778,0.0007040458],"domain_scores_gemma":[0.9972135,0.0004003056,0.0004097588,0.0004579806,0.0004810374,0.001037459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003787004,0.00004308316,0.9321087,0.002291356,0.001515471,0.00005451257,0.03089575,0.00002913799,0.00000499217,0.002023645,0.008185099,0.02281036],"study_design_scores_gemma":[0.00191768,0.0004420104,0.8373036,0.003320501,0.005167902,0.0004747469,0.1074384,0.01034514,6.592749e-7,0.001300239,0.03098897,0.001300117],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825649,0.002696538,0.00005507792,0.00448115,0.00001898477,0.0005622053,0.009269986,0.000009165358,0.0003420114],"genre_scores_gemma":[0.9965441,0.00167376,0.0004022749,0.0007967927,0.0001109648,0.00003624219,0.00016647,0.00002157767,0.0002478233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09480511,"threshold_uncertainty_score":0.7670208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1063952947770415,"score_gpt":0.3218664416498453,"score_spread":0.2154711468728039,"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."}}