{"id":"W4398470125","doi":"10.7910/dvn/dqltvj/gelreu","title":"representation.do","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Legislator; Replication (statistics); Representation (politics); Political science; Computer science; Internet privacy; Law; Mathematics; Statistics; Politics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00128173,0.002758669,0.001863471,0.005608356,0.001020364,0.005006181,0.003726956,0.002797518,0.2182363],"category_scores_gemma":[0.008361027,0.0009804827,0.001525807,0.009343299,0.000646146,0.002637691,0.003509097,0.002440164,0.2739333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001607583,"about_ca_system_score_gemma":0.002050661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02232415,"about_ca_topic_score_gemma":0.03405101,"domain_scores_codex":[0.9986424,0.0002923055,0.0001326931,0.0003842891,0.0002707125,0.0002775819],"domain_scores_gemma":[0.9971214,0.0006821073,0.0003284535,0.001002874,0.0004368083,0.0004283225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003550197,0.00001100643,0.0003820001,0.0003173537,0.00001640234,0.000006108793,0.00001535242,0.0001101288,0.00002678396,0.0004977424,0.9970164,0.001565239],"study_design_scores_gemma":[0.0001937013,0.00001338383,0.001588665,0.0002995747,0.00001760797,0.00002181901,0.00005642552,0.0003376331,0.0001490287,0.00176988,0.9955316,0.00002076107],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005863512,0.0000560871,0.00003957051,0.00008661445,0.00003176107,0.000005335329,0.9981993,0.0004742861,0.001048308],"genre_scores_gemma":[0.0004659346,0.00007710577,0.0001421145,0.00008865201,0.00001685981,0.00004989721,0.9979372,0.0001519313,0.001070251],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7817637,"threshold_uncertainty_score":0.7300731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06768509939762982,"score_gpt":0.3638948209405656,"score_spread":0.2962097215429358,"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."}}