{"id":"W3037175677","doi":"10.2139/ssrn.3427482","title":"How Data Gaps (re)Make Rural Broadband Gaps","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"ICT Impact and Policies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Guelph","funders":"","keywords":"Broadband; Business; Telecommunications; Geography; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005444507,0.0002127998,0.0002265914,0.0001163217,0.00009317722,0.0002162199,0.0006492996,0.00009489951,0.00009953709],"category_scores_gemma":[0.00002320651,0.0001841118,0.00007748366,0.0001468574,0.00002234663,0.0004899575,0.00006912591,0.001567916,0.000185022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003678084,"about_ca_system_score_gemma":0.000390999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003968561,"about_ca_topic_score_gemma":0.0003491739,"domain_scores_codex":[0.9971468,0.00002901941,0.0001776167,0.00007653543,0.0002395391,0.002330424],"domain_scores_gemma":[0.9992666,0.00003259585,0.00005114028,0.000506308,0.00002815232,0.0001151892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005757886,0.0004118089,0.07696661,0.0007992371,0.007075585,0.00005220418,0.01896236,0.01405913,0.1487502,0.2075903,0.1854734,0.3392833],"study_design_scores_gemma":[0.006929877,0.001436387,0.007731686,0.0003465577,0.0004061259,0.005572179,0.03608418,0.009253606,0.007511419,0.06015269,0.8615003,0.003074958],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756873,0.01434781,0.0008959783,0.001078707,0.0009459517,0.000124014,0.00002876132,0.0001606584,0.006730848],"genre_scores_gemma":[0.9821159,0.007027843,0.00001717796,0.00007945582,0.0007478851,0.000001024467,0.0000369972,0.00005474335,0.009919011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6760269,"threshold_uncertainty_score":0.7507862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009995103931814155,"score_gpt":0.2318586944037104,"score_spread":0.2218635904718962,"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."}}