{"id":"W4231037590","doi":"10.1111/ecin.2016.54.issue-1","title":"RECONCILING MICRO AND MACRO ESTIMATES OF THE FRISCH LABOR SUPPLY ELASTICITY","year":2016,"lang":"en","type":"paratext","venue":"Economic Inquiry","topic":"Asian Industrial and Economic Development","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Economics; Mathematical economics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007920847,0.0002614279,0.0005564939,0.0000762884,0.0004541072,0.00009664503,0.0005562014,0.0004969381,0.008477164],"category_scores_gemma":[0.0001561075,0.0001969759,0.0001209752,0.00004328067,0.001027394,0.000163163,0.0002512823,0.0002784911,0.003479189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005475032,"about_ca_system_score_gemma":0.00149688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001161185,"about_ca_topic_score_gemma":0.0005291682,"domain_scores_codex":[0.9982494,0.0001166713,0.0006421964,0.0004618262,0.00007609847,0.0004538047],"domain_scores_gemma":[0.9986089,0.0003821907,0.0005658652,0.0002803713,0.00003911073,0.0001235527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005000779,0.00001883314,0.01297842,0.0000794799,0.0002142081,0.00000109663,0.01257992,0.00002805245,0.00008734262,0.001311637,0.9595451,0.01310592],"study_design_scores_gemma":[0.0004906443,0.00001819772,0.003062693,0.0003303835,0.0000384393,0.000002618836,0.00239245,0.000009784817,0.001543675,0.001029343,0.9906345,0.0004472758],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6555522,0.001277365,0.0000351114,0.004880278,0.05210702,0.000937889,0.001218998,0.00003690402,0.2839542],"genre_scores_gemma":[0.3521082,0.004800115,0.00138417,0.0008468261,0.02300107,0.0001389575,0.0000867035,0.0001586365,0.6174754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3335211,"threshold_uncertainty_score":0.9972967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03742762364101145,"score_gpt":0.2958632771202242,"score_spread":0.2584356534792128,"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."}}