{"id":"W3147784747","doi":"","title":"Digital Divide: From Computer Access to Online Activities – A Micro Data Analysis","year":2011,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"ICT Impact and Policies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital divide; The Internet; Inequality; Information and Communications Technology; Internet access; Computer science; Telecommunications; Data science; World Wide Web; Mathematics","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":[0.001588033,0.0001975716,0.0004163061,0.005093759,0.00064601,0.001917626,0.0004372906,0.0004219239,0.003362936],"category_scores_gemma":[0.007725344,0.0001650963,0.0003416088,0.01060373,0.0008619443,0.001925827,0.001405091,0.0007091786,0.0004485687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008867977,"about_ca_system_score_gemma":0.0005602821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01273029,"about_ca_topic_score_gemma":0.01040059,"domain_scores_codex":[0.9975948,0.0008968484,0.0002041348,0.0003613356,0.0007347167,0.0002081304],"domain_scores_gemma":[0.9896954,0.006120228,0.001928663,0.0008974251,0.001009972,0.0003483651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001598458,0.0001695169,0.9423473,0.0001901875,0.0001871898,0.0002404411,0.004045122,0.001378521,0.0004567986,0.009655287,0.003190173,0.03797955],"study_design_scores_gemma":[0.00001839699,0.0002025631,0.9393713,0.00014915,0.00011994,0.000358775,0.01844862,0.01001765,0.001921938,0.009291801,0.02005406,0.00004570373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669795,0.0005777079,0.008545432,0.0008394843,0.00002484998,0.0002156945,0.01219082,0.00003735482,0.0105892],"genre_scores_gemma":[0.9896314,0.0002304429,0.004228376,0.00008760542,0.00003011109,0.0001630276,0.00439833,0.00001098607,0.001219857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01273029,"threshold_uncertainty_score":0.02531242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08624391508506021,"score_gpt":0.3482567840622008,"score_spread":0.2620128689771406,"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."}}