{"id":"W4288095783","doi":"10.5281/zenodo.3970442","title":"Voice and accountability and information technology for Latin America","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"ICT Impact and Policies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Accountability; Latin Americans; Political science; Business; 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":[0.001525619,0.0001640526,0.0001823967,0.001602541,0.0008180544,0.002639293,0.0001398474,0.0003489093,0.003408291],"category_scores_gemma":[0.004715705,0.0000482434,0.000287154,0.00244699,0.001664659,0.00104695,0.001560778,0.0004422921,0.0001177864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002386168,"about_ca_system_score_gemma":0.001806027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01977401,"about_ca_topic_score_gemma":0.01518042,"domain_scores_codex":[0.9988938,0.0005341955,0.00004908965,0.00009696273,0.0001452298,0.0002807232],"domain_scores_gemma":[0.9973766,0.001482365,0.000602584,0.00009808315,0.000293008,0.0001475163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005379615,0.0003234819,0.6531551,0.0004220661,0.0001468222,0.000541941,0.02531756,0.002387945,0.002825265,0.1591979,0.002070868,0.153073],"study_design_scores_gemma":[0.00003353182,0.0002117244,0.888164,0.0003272483,0.00008779085,0.0001986663,0.04433177,0.002544181,0.0008601433,0.02117091,0.0420366,0.00003337702],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.919118,0.002319402,0.0009971265,0.003125284,0.00003827501,0.00002118025,0.0001441132,0.00003034084,0.07420623],"genre_scores_gemma":[0.9981058,0.0004262517,0.0002238216,0.0001131604,0.00001583106,0.0000133452,0.00004713386,0.000004572919,0.001050106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01977401,"threshold_uncertainty_score":0.03931785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01221171113875242,"score_gpt":0.2211874270825311,"score_spread":0.2089757159437787,"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."}}