{"id":"W1715474456","doi":"10.3968/j.css.1923669720141001.2789","title":"The Legal Framework of Electronic Data Crimes","year":2014,"lang":"en","type":"article","venue":"Canadian social science","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Commit; The Internet; Computer science; Computer security; Confidentiality; Electronic data; Internet privacy; Process (computing); Business; World Wide Web; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.007230148,0.0004696276,0.0005696008,0.005813038,0.009793065,0.01186147,0.002254525,0.005920687,0.007697905],"category_scores_gemma":[0.01485305,0.0003523628,0.0005701388,0.003061095,0.03932728,0.007902449,0.005099985,0.005600591,0.000773008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01240688,"about_ca_system_score_gemma":0.01424779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06249006,"about_ca_topic_score_gemma":0.02550272,"domain_scores_codex":[0.9867224,0.004531606,0.0008991638,0.00167903,0.004280049,0.001887774],"domain_scores_gemma":[0.9894698,0.004880248,0.001323281,0.000782255,0.002774336,0.0007700811],"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":[5.003853e-7,0.000004265348,0.00008900926,0.000005048328,3.834938e-7,0.0000322722,0.0003419738,0.000028734,0.000006559029,0.9979742,0.0007415602,0.0007753932],"study_design_scores_gemma":[0.00001204839,0.00001360567,0.001286475,0.0003607184,0.000006451508,0.0002826261,0.003738894,0.0008982151,0.0001002283,0.8727023,0.1205703,0.00002810252],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02469749,0.009204718,0.01972714,0.05679896,0.0006776067,0.0002504637,0.0003674128,0.00004134194,0.8882349],"genre_scores_gemma":[0.9304596,0.006537229,0.01127995,0.007836184,0.0008696051,0.0004259627,0.0002689484,0.00003519048,0.04228726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06249006,"threshold_uncertainty_score":0.1242526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752312633439005,"score_gpt":0.2773208738682732,"score_spread":0.2597977475338831,"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."}}