{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00465089,0.0004080743,0.0006577383,0.001709659,0.004541367,0.007534679,0.00157128,0.002809895,0.01317021],"category_scores_gemma":[0.01962088,0.0003805379,0.0004123283,0.001549128,0.00985174,0.005564234,0.00428076,0.004284612,0.0007340183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00463926,"about_ca_system_score_gemma":0.005562859,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03943254,"about_ca_topic_score_gemma":0.04794394,"domain_scores_codex":[0.9952638,0.001926134,0.000171331,0.0004666155,0.001130172,0.001041927],"domain_scores_gemma":[0.9879436,0.006714246,0.001713329,0.001468686,0.001519173,0.000640877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006186894,0.00002223018,0.00273707,0.00004135321,0.00001317945,0.0003379592,0.001524543,0.0007738561,0.00005098839,0.9651806,0.01567746,0.013579],"study_design_scores_gemma":[0.0000405845,0.00001644592,0.00445826,0.0002436763,0.00002639625,0.0002670265,0.003837673,0.002284553,0.0004916979,0.9150327,0.07326527,0.00003565103],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1906869,0.008024927,0.02260335,0.1103316,0.0005241863,0.0001923139,0.0009458507,0.0002576985,0.6664333],"genre_scores_gemma":[0.9501669,0.001924996,0.00619409,0.003820948,0.0001418401,0.0001278718,0.0002447125,0.00007144763,0.03730726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9605675,"threshold_uncertainty_score":0.07840598,"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."}}