{"id":"W4398274863","doi":"10.7910/dvn/k0oyqf/hiqwhn","title":"canada200","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Word (group theory); Ideology; Linguistics; Word length; Natural language processing; Computer science; Political science; Philosophy; Politics; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001032366,0.002222223,0.001351908,0.005549075,0.002136822,0.003899917,0.002818478,0.001458957,0.2278627],"category_scores_gemma":[0.006858706,0.0007818621,0.001057608,0.01086352,0.0007254152,0.001018371,0.001847568,0.001488886,0.266153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003938457,"about_ca_system_score_gemma":0.01103782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3791104,"about_ca_topic_score_gemma":0.5224187,"domain_scores_codex":[0.9987399,0.0001515788,0.0000953264,0.0004055197,0.0003389451,0.0002686996],"domain_scores_gemma":[0.9959453,0.0005851607,0.0002271611,0.001149146,0.001430664,0.0006625917],"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":[0.00005229689,0.00001236725,0.00036617,0.000136976,0.000009303598,0.000009633223,0.00001396151,0.00008645924,0.00006064515,0.0004398361,0.9956781,0.003134178],"study_design_scores_gemma":[0.00008953681,0.000006383033,0.001913722,0.0001041993,0.00001458333,0.00002141582,0.00005838888,0.0001882075,0.0002364957,0.0009105461,0.996439,0.00001746374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001837255,0.0000547868,0.00006201375,0.00005884445,0.00003054738,0.0000118389,0.9955335,0.0004750085,0.003589644],"genre_scores_gemma":[0.0005819709,0.00007063166,0.0002548587,0.00008411764,0.000010038,0.00004234702,0.9921384,0.0002432964,0.006574231],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7721373,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04332124749979459,"score_gpt":0.3451813906196236,"score_spread":0.3018601431198289,"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."}}