{"id":"W3121485159","doi":"","title":"Deterrence: Increased Enforcement versus Harsher Penalties","year":2012,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Deterrence (psychology); Enforcement; Business; Law enforcement; Computer security; Economics; Political science; Law and economics; Computer science; Law","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":[0.004573697,0.0004660861,0.0006846964,0.0008607948,0.0006089217,0.002739761,0.0006122365,0.001869018,0.005517928],"category_scores_gemma":[0.02012048,0.0002867731,0.0004247271,0.0006417184,0.003199979,0.002505674,0.002144789,0.002162803,0.0003153969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105256,"about_ca_system_score_gemma":0.000696424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001074463,"about_ca_topic_score_gemma":0.001890827,"domain_scores_codex":[0.9939715,0.002601605,0.0005511987,0.0009590102,0.001097738,0.0008189307],"domain_scores_gemma":[0.9753394,0.009364198,0.01052627,0.001857336,0.00164208,0.001270861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.003214612,0.003645425,0.3536299,0.001505746,0.000841627,0.001434851,0.003497612,0.01677204,0.01048044,0.3939341,0.004743372,0.2063003],"study_design_scores_gemma":[0.0008925738,0.005082784,0.8195122,0.0006073514,0.0005894098,0.001861524,0.004919814,0.01603288,0.003821916,0.1231729,0.0233103,0.0001962985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9145207,0.002190483,0.00566366,0.004751974,0.0002050423,0.0001571807,0.0001509566,0.00002907689,0.07233084],"genre_scores_gemma":[0.9963994,0.0002849127,0.001120435,0.0005657098,0.0001109836,0.00003132574,0.00002741821,0.000007092258,0.00145265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005517928,"threshold_uncertainty_score":0.02418834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0280445829998385,"score_gpt":0.3101646920732704,"score_spread":0.2821201090734319,"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."}}