{"id":"W4254845866","doi":"10.32920/ryerson.14640066","title":"Automation Across the Nation: Understanding the potential impacts of technological trends across Canada","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Research, Science, and Academia","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Public Works and Government Services Canada; University of Toronto","keywords":"Productivity; Automation; Consumption (sociology); Technological change; Worry; Emerging technologies; Economics; Business; Engineering; Economic growth; Computer science; Artificial intelligence; Sociology; Social science; Macroeconomics; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01234297,0.0002297911,0.0003877363,0.0001004747,0.001417427,0.00190037,0.004126053,0.0003827476,0.0006896777],"category_scores_gemma":[0.004907093,0.00009601517,0.0002393301,0.002409228,0.001455141,0.0003186523,0.002834096,0.001361081,0.000006399402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005484459,"about_ca_system_score_gemma":0.001172643,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0993969,"about_ca_topic_score_gemma":0.4434773,"domain_scores_codex":[0.9918352,0.0005075905,0.000921931,0.0007640804,0.005284287,0.0006868801],"domain_scores_gemma":[0.9958465,0.001684117,0.0005924653,0.001228149,0.0005220208,0.0001267465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001355793,0.0002327963,0.01375392,0.0001129561,0.0003788235,0.0002250155,0.02138139,0.05737919,0.005706435,0.06039384,0.1155957,0.7247044],"study_design_scores_gemma":[0.000914183,0.00009417265,0.2590822,0.0002220502,0.00003966165,0.0001531915,0.4031158,0.1642369,0.008078201,0.1520147,0.01095406,0.001094826],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9068466,0.0003406316,0.03928506,0.04324606,0.001288828,0.0003775128,0.0001647122,0.00008780909,0.008362733],"genre_scores_gemma":[0.9971223,0.00005947115,0.000101453,0.0002324494,0.0001000206,0.00001612055,0.00001220854,0.000006727328,0.002349291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7236095,"threshold_uncertainty_score":0.9998826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1979969952557548,"score_gpt":0.4505442574932667,"score_spread":0.2525472622375118,"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."}}