{"id":"W4404128062","doi":"10.2139/ssrn.5009388","title":"Assessing the Impact of Generative AI on Canadian Labor Market: An Empirical Approach","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Canada West; Yorkville University","funders":"","keywords":"Generative grammar; Economics; Industrial organization; Labour economics; Business; Artificial intelligence; Computer science","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":[0.002767774,0.0005180089,0.0005201161,0.002392651,0.00252745,0.003279744,0.001684805,0.000925324,0.008990628],"category_scores_gemma":[0.01925733,0.0002006472,0.0006513331,0.003417533,0.002107439,0.001079817,0.00119442,0.001280649,0.000481594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02944701,"about_ca_system_score_gemma":0.02874602,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9473132,"about_ca_topic_score_gemma":0.9398525,"domain_scores_codex":[0.9986625,0.0002547385,0.00003230114,0.0001668919,0.0004819477,0.0004017522],"domain_scores_gemma":[0.9857271,0.007792747,0.001187157,0.0008765991,0.003084148,0.00133219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001623665,0.001207253,0.6756908,0.0003306735,0.0004221568,0.0003579571,0.004270523,0.09374246,0.003681775,0.1045323,0.006721003,0.1074195],"study_design_scores_gemma":[0.0001494383,0.0005712158,0.836309,0.00007960457,0.0004366056,0.0001001209,0.007513329,0.1219217,0.002165397,0.01618863,0.01441209,0.0001527837],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9696525,0.0004341428,0.001220847,0.0005757126,0.00001442714,0.0000821503,0.0008515252,0.0000575301,0.02711116],"genre_scores_gemma":[0.9962291,0.000183036,0.0004332217,0.00005489461,0.00001069671,0.00001397498,0.0003534344,0.000007930174,0.002713708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05268681,"threshold_uncertainty_score":0.213654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04375430793199208,"score_gpt":0.3292409212210446,"score_spread":0.2854866132890526,"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."}}