{"id":"W4206155640","doi":"10.1109/mcom.001.2100478","title":"Enabling Universal Connectivity via Data-Driven Policymaking: A North American Case Study","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Magazine","topic":"ICT Impact and Policies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Digital divide; Computer science; Leverage (statistics); Big data; Data science; Broadband; Internet access; Work (physics); The Internet; Business model; Telecommunications; Internet privacy; Business; World Wide Web; Marketing; Artificial intelligence","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":[0.005107641,0.000338756,0.0002593948,0.001147119,0.004784155,0.003246093,0.001409313,0.002501505,0.002767106],"category_scores_gemma":[0.01068425,0.000205515,0.000377673,0.00321502,0.002302766,0.002478829,0.002613958,0.002379922,0.0003710972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005357349,"about_ca_system_score_gemma":0.006274224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1316482,"about_ca_topic_score_gemma":0.2028334,"domain_scores_codex":[0.9957134,0.002878923,0.0001120472,0.0002773466,0.000445946,0.0005724319],"domain_scores_gemma":[0.9894252,0.007089519,0.0006063397,0.0008559351,0.001217553,0.0008053959],"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":[0.0006425928,0.005953653,0.243698,0.0007838927,0.0002193546,0.03590015,0.08774299,0.06580723,0.002855504,0.2747985,0.1193509,0.1622472],"study_design_scores_gemma":[0.0003429338,0.0005126885,0.0976475,0.0008316435,0.0001290932,0.002238124,0.3430529,0.1551813,0.002435975,0.09442817,0.3029539,0.0002456823],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8877036,0.0006360327,0.01021814,0.02939748,0.0001455594,0.0004640923,0.00114947,0.00009262568,0.07019301],"genre_scores_gemma":[0.9860029,0.0005728556,0.006664589,0.001808095,0.00005298591,0.0002741753,0.0003747639,0.00002698884,0.004222522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1316482,"threshold_uncertainty_score":0.2617639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06479974436392467,"score_gpt":0.3266292456719337,"score_spread":0.2618295013080091,"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."}}