{"id":"W2485304376","doi":"10.4018/978-1-60960-848-4.ch003","title":"E-Government Initiatives","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Section (typography); Government (linguistics); Context (archaeology); Extension (predicate logic); Regional science; Political science; Developing country; Public administration; Economic growth; Computer science; Geography; Economics","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":[0.001677454,0.0003553451,0.0001863235,0.001631804,0.002109379,0.004648594,0.0008473485,0.00146463,0.01763494],"category_scores_gemma":[0.003013257,0.0001519268,0.0003679738,0.005295308,0.001900736,0.004996466,0.004467358,0.001541274,0.003042895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002478249,"about_ca_system_score_gemma":0.003170992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00113002,"about_ca_topic_score_gemma":0.00280865,"domain_scores_codex":[0.997346,0.0012986,0.00011369,0.0002471757,0.0005952559,0.000399219],"domain_scores_gemma":[0.9982674,0.0008768543,0.0001873374,0.0001672791,0.0002358705,0.0002652674],"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":[0.0000233122,0.0003599135,0.005139003,0.0007139911,0.000009704024,0.0006659871,0.02210557,0.0006008765,0.0006012502,0.7469887,0.02468586,0.1981059],"study_design_scores_gemma":[0.000007809414,0.00008402857,0.005451245,0.0007598697,0.00001007579,0.0008403675,0.03079955,0.0002955251,0.0009880277,0.01833619,0.9424101,0.0000172087],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.07074064,0.00438118,0.005362722,0.003371083,0.0002325366,0.0002306029,0.0002033554,0.0001023691,0.9153755],"genre_scores_gemma":[0.8279077,0.01344487,0.014394,0.001829991,0.0001347959,0.0004566899,0.0007442564,0.00007896815,0.1410088],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01763494,"threshold_uncertainty_score":0.05899471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03532508278619671,"score_gpt":0.2754225333268486,"score_spread":0.2400974505406519,"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."}}