{"id":"W2485512915","doi":"10.4018/978-1-59904-947-2.ch026","title":"Current Approaches to Federal E-Government","year":2008,"lang":"en","type":"book-chapter","venue":"Electronic Government","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Variety (cybernetics); Scope (computer science); Politics; Public administration; Political science; Business; China; Economic growth; Public relations; Economics; Law","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":[0.005508686,0.0004621448,0.0004563196,0.004097019,0.008912929,0.01306586,0.002897139,0.006754583,0.04933119],"category_scores_gemma":[0.01148152,0.0004830744,0.001154915,0.006704473,0.005100273,0.01039365,0.006226285,0.004870592,0.008940325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02066039,"about_ca_system_score_gemma":0.01970977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06353042,"about_ca_topic_score_gemma":0.07626656,"domain_scores_codex":[0.9923449,0.002303086,0.000450954,0.00116091,0.00191854,0.00182163],"domain_scores_gemma":[0.9950831,0.001289717,0.0002611045,0.001072223,0.001572265,0.00072171],"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":[0.00001002331,0.00003617557,0.0006111407,0.0001306906,0.000008038964,0.00005156104,0.0007417914,0.0002524555,0.00005029765,0.9243043,0.03621175,0.03759179],"study_design_scores_gemma":[0.00001043847,0.00002430928,0.002368467,0.0005485999,0.00001549137,0.000158795,0.002089953,0.0008504125,0.0001792752,0.1094218,0.8843069,0.00002556922],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.008696525,0.008361485,0.01194763,0.07468677,0.001449896,0.0001690116,0.0004549677,0.0004952088,0.8937386],"genre_scores_gemma":[0.5171801,0.02057531,0.02720237,0.05157224,0.001851946,0.000726646,0.001906024,0.0003830942,0.3786022],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.06353042,"threshold_uncertainty_score":0.1650293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06516958166071442,"score_gpt":0.2529655935138286,"score_spread":0.1877960118531142,"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."}}