{"id":"W3204528404","doi":"","title":"Lead-lag between female employment and economic growth: evidence from Canada","year":2017,"lang":"en","type":"preprint","venue":"Munich Personal RePEc Archive (Munich University)","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Casual; Economics; Causality (physics); Business cycle; Labour economics; Demographic economics; Macroeconomics; Political science","routes":{"ca_aff":false,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006475857,0.0006558262,0.001364203,0.0004854402,0.000821014,0.0002578596,0.002213527,0.0003467237,0.0002701273],"category_scores_gemma":[0.000347841,0.0009323756,0.0003295988,0.00006924399,0.0004959612,0.0006176759,0.003629629,0.001358785,0.0001239099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001552817,"about_ca_system_score_gemma":0.001240288,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8524476,"about_ca_topic_score_gemma":0.5858165,"domain_scores_codex":[0.9963276,0.0001672756,0.0006792533,0.002003971,0.0001015234,0.0007203329],"domain_scores_gemma":[0.9956015,0.0007452515,0.001183954,0.001949338,0.00006049258,0.0004593999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001717799,0.0000665455,0.9790071,0.0002120005,0.001024301,0.0001456618,0.003011468,0.00007534333,0.00001497973,0.01178886,0.002384625,0.00209735],"study_design_scores_gemma":[0.002310261,0.0002553407,0.7351071,0.001149655,0.0003563075,0.00001592262,0.00130886,0.004143259,0.0001981884,0.05723015,0.1942165,0.003708474],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9515452,0.005128744,0.0003164602,0.002119982,0.0009021345,0.0005281618,0.007491851,0.00006090739,0.03190652],"genre_scores_gemma":[0.9848981,0.006539488,0.001427169,0.00009098006,0.0005805551,0.000007034346,0.0004123484,0.00006787539,0.005976441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2666312,"threshold_uncertainty_score":0.9993127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0639533251685259,"score_gpt":0.2215059905493566,"score_spread":0.1575526653808307,"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."}}