{"id":"W2808244501","doi":"10.2139/ssrn.3076971","title":"Labor Force Demographics and Corporate Innovation","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Demographics; Business; Demographic economics; Economics; Demography; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0009343855,0.0001023268,0.0001654588,0.001124084,0.0004572621,0.001256595,0.0001767667,0.0005891712,0.008780803],"category_scores_gemma":[0.003860344,0.0001130608,0.0002047233,0.0008665827,0.0003106496,0.0008079689,0.0005755452,0.0005061417,0.001242332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002770892,"about_ca_system_score_gemma":0.0004713956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005228844,"about_ca_topic_score_gemma":0.01118524,"domain_scores_codex":[0.9996438,0.0001061956,0.00001590549,0.00002976388,0.00004132766,0.0001630253],"domain_scores_gemma":[0.9954628,0.001711254,0.00129803,0.0001528557,0.0002466337,0.00112844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007339617,0.0001149529,0.9837319,0.00001086771,0.0000233105,0.0001211158,0.0007730258,0.0001938751,0.0001793184,0.002025103,0.001006387,0.01174673],"study_design_scores_gemma":[0.000009729343,0.00009478044,0.9901593,0.00003107893,0.0000170788,0.0001199245,0.003791043,0.0004832399,0.00008014172,0.002281179,0.002926123,0.000006378803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938419,0.0006473052,0.0001410603,0.0009899049,0.00001122517,0.000007862831,0.000255042,0.000003162319,0.004102603],"genre_scores_gemma":[0.9962166,0.000328968,0.00002580394,0.00007498331,0.00003420502,0.000005892743,0.0001621748,0.000002023804,0.003149378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008780803,"threshold_uncertainty_score":0.02937472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02597883382773248,"score_gpt":0.2187876959593293,"score_spread":0.1928088621315968,"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."}}