{"id":"W2578588058","doi":"10.1017/s0008423916001165","title":"Digitization of the Canadian Parliamentary Debates","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Political Science","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Digitization; House of Commons; Political science; Architecture; Politics; Commons; Public administration; Library science; Media studies; Computer science; Sociology; Geography; Law; Telecommunications; Parliament; Archaeology","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":[],"consensus_categories":[],"category_scores_codex":[0.005077076,0.0005124256,0.0005949807,0.03112714,0.01040064,0.007858695,0.002159298,0.000698077,0.01927991],"category_scores_gemma":[0.02124201,0.0005188895,0.0004914409,0.05680427,0.00385233,0.002139044,0.00470705,0.001820757,0.001354613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1010198,"about_ca_system_score_gemma":0.1204231,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9830294,"about_ca_topic_score_gemma":0.9907219,"domain_scores_codex":[0.9922996,0.000698328,0.0002957454,0.0009130543,0.004726343,0.001066992],"domain_scores_gemma":[0.9835872,0.002627538,0.001006349,0.002406132,0.009381157,0.0009916053],"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":[0.0002663651,0.00004397941,0.02684791,0.001357719,0.00008202263,0.0005867568,0.05895795,0.002318987,0.002226836,0.1466528,0.2465142,0.5141445],"study_design_scores_gemma":[0.000007339689,0.000005987213,0.0527788,0.0004545281,0.00003688919,0.0001132841,0.005993109,0.0003053505,0.001174382,0.001848808,0.937219,0.00006251618],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1929832,0.02245297,0.01250826,0.01661532,0.001715032,0.0009415001,0.1929654,0.001278863,0.5585393],"genre_scores_gemma":[0.758527,0.01451429,0.02785986,0.00211036,0.0004903103,0.0008148491,0.07882314,0.0007846752,0.1160755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1010198,"threshold_uncertainty_score":0.7329537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04302063556107077,"score_gpt":0.3648532980313726,"score_spread":0.3218326624703018,"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."}}