{"id":"W4400977443","doi":"10.1371/journal.pone.0302457","title":"Streamlining Canadian parliamentary data access: A user-friendly R package","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École Nationale d'Administration Publique","funders":"","keywords":"Open government; Open data; Openness to experience; Commons; Government (linguistics); Computer science; Flexibility (engineering); Data science; Scalability; World Wide Web; User Friendly; Adaptability; Process (computing); Metadata; Data curation; Political science; Database","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007783689,0.00007480398,0.0001358114,0.0001591235,0.0003301865,0.0004654427,0.000669975,0.00004145735,0.001689263],"category_scores_gemma":[0.0002642986,0.00007333299,0.00003633222,0.0005837041,0.00006960398,0.0006285566,0.0001517712,0.0001253374,0.0001689261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253649,"about_ca_system_score_gemma":0.0006848457,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4236603,"about_ca_topic_score_gemma":0.7384762,"domain_scores_codex":[0.998664,0.0001656949,0.0001489566,0.0003148128,0.0004432384,0.0002632595],"domain_scores_gemma":[0.9990515,0.0003559837,0.00002484098,0.0002803971,0.00005035316,0.0002369892],"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":[0.00002934689,0.001761069,0.1839141,0.0004476549,0.00580425,0.000763045,0.02609246,0.00008632403,0.0006991078,0.08586472,0.1510095,0.5435284],"study_design_scores_gemma":[0.0004824702,0.0001316607,0.02478195,0.001365156,0.002214822,0.000001898719,0.01478454,0.04006653,0.0006454799,0.03465865,0.8794531,0.001413775],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8174589,0.003757524,0.005869822,0.04069614,0.0007598941,0.0006973291,0.001013452,0.0006806292,0.1290663],"genre_scores_gemma":[0.9806396,0.0001568399,0.01507914,0.0004695418,0.0007007153,0.00001085028,0.0003087052,0.00001442715,0.002620176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7284435,"threshold_uncertainty_score":0.9992234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2122364220233029,"score_gpt":0.3990786370595693,"score_spread":0.1868422150362664,"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."}}