{"id":"W4226128343","doi":"10.2139/ssrn.4054023","title":"How do households spend carbon tax rebates?","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Ottawa; Global Affairs Canada","funders":"","keywords":"Carbon tax; Business; Economics; Public economics; Greenhouse gas","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007861474,0.0002082235,0.0004279179,0.0003329326,0.0002895719,0.001603948,0.0003827598,0.00139266,0.01086657],"category_scores_gemma":[0.007963204,0.0002087816,0.0002663702,0.0007509001,0.0004689213,0.00209387,0.0002641623,0.0009215021,0.001383062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001205331,"about_ca_system_score_gemma":0.0007967603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02045319,"about_ca_topic_score_gemma":0.03420895,"domain_scores_codex":[0.9995722,0.0001993707,0.00001433457,0.00005909084,0.00004376149,0.0001113105],"domain_scores_gemma":[0.9977043,0.001176751,0.0005615783,0.00007008528,0.0002409366,0.0002462824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000743931,0.0007207457,0.5434213,0.0002984157,0.0006776619,0.0005195675,0.001184677,0.0816847,0.001082394,0.1038104,0.06083942,0.2050168],"study_design_scores_gemma":[0.000127938,0.0004013961,0.5035277,0.0002444991,0.0004392711,0.0005254555,0.01464847,0.1546578,0.001737527,0.2519083,0.07162998,0.0001516706],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8732774,0.002089587,0.008253417,0.04888123,0.0003529104,0.00008383414,0.007439213,0.00008907737,0.05953322],"genre_scores_gemma":[0.9910955,0.0007357035,0.0004494765,0.0008230581,0.00005744461,0.00002209755,0.0005689471,0.00001058149,0.006237145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02045319,"threshold_uncertainty_score":0.04066825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0722831244356398,"score_gpt":0.321104861777708,"score_spread":0.2488217373420682,"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."}}