{"id":"W3206990469","doi":"10.11575/prism/39097","title":"Pressing Its Luck: How Ontario Lottery and Gaming Can Work For, Not Against, Low-Income Households","year":2020,"lang":"en","type":"article","venue":"PRISM (University of Calgary)","topic":"Housing, Finance, and Neoliberalism","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Luck; Lottery; Work (physics); Business; Economics; Labour economics; Engineering","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":[0.0001293477,0.0001662689,0.0004536413,0.0001127263,0.0002315772,0.00005271122,0.0002455592,0.0001455671,0.00003050905],"category_scores_gemma":[0.00001177468,0.0002425167,0.0001307392,0.0001504868,0.00009813302,0.0003404526,0.000142433,0.0001932801,0.00001009133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001153085,"about_ca_system_score_gemma":0.00005144913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002690879,"about_ca_topic_score_gemma":0.0003117509,"domain_scores_codex":[0.9990199,0.00001119203,0.0001936074,0.0004230597,0.00004969252,0.0003025097],"domain_scores_gemma":[0.999287,0.00003844824,0.0003042646,0.0001847909,0.00002644527,0.0001590472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001530368,0.0004965395,0.4172514,0.002240239,0.0009098785,0.0005569821,0.1748792,0.00009804016,0.003365387,0.02406954,0.0156901,0.3589123],"study_design_scores_gemma":[0.005923958,0.0003188543,0.5863426,0.0004398231,0.00009970104,0.000006264333,0.0001510529,0.03837517,0.001171158,0.0006360231,0.3648761,0.001659298],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401298,0.0005765596,0.05005369,0.00190021,0.0001520137,0.0002354548,0.00001051614,0.00005572986,0.006886018],"genre_scores_gemma":[0.9836289,0.0002140238,0.01142015,0.0006770447,0.00004393881,7.151244e-7,0.00001101673,0.00002870287,0.003975506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.357253,"threshold_uncertainty_score":0.9889544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02900815509481969,"score_gpt":0.1732857376075337,"score_spread":0.1442775825127141,"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."}}