{"id":"W7008242338","doi":"","title":"Bad Laws Make Hard Cases: Halifax and the avoidance of inconsistent tax rules.","year":2014,"lang":"en","type":"report","venue":"e-Archivo (Carlos III University of Madrid)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Raising (metalworking); Tax avoidance; Scope (computer science); Punitive damages; Tax law; Preference; Deterrence (psychology)","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003206519,0.0009914249,0.002793911,0.0007671955,0.0005767227,0.00005507038,0.001873553,0.0005494449,0.0003306619],"category_scores_gemma":[0.0008072171,0.0009162361,0.001177642,0.0004017549,0.005486425,0.0001781507,0.001842897,0.001425126,0.0002720993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005842134,"about_ca_system_score_gemma":0.001766791,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04118344,"about_ca_topic_score_gemma":0.01694977,"domain_scores_codex":[0.9931992,0.001230699,0.001068275,0.001288659,0.002396395,0.0008167385],"domain_scores_gemma":[0.9920945,0.001326813,0.002798493,0.002336746,0.001041713,0.0004017769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01600669,0.001801537,0.01804898,0.01249256,0.01091516,0.004334467,0.01426964,0.0003007787,0.002572779,0.01253723,0.8818936,0.02482656],"study_design_scores_gemma":[0.01202876,0.0004055475,0.0216829,0.003576916,0.003455782,0.001273377,0.002495958,0.0003005732,0.0003005209,0.0008620783,0.9518644,0.001753167],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3900391,0.008124013,0.001480003,0.001595892,0.002164421,0.006076526,0.03595175,0.0006439519,0.5539243],"genre_scores_gemma":[0.8994904,0.007730559,0.00573901,0.0001462637,0.0006441092,0.000008388351,0.001213221,0.0005284636,0.08449966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5094512,"threshold_uncertainty_score":0.9993289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02449872316296833,"score_gpt":0.2291709499465283,"score_spread":0.20467222678356,"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."}}