{"id":"W6910086783","doi":"10.3886/e170261v1","title":"Data and Code for: Labor Market Responses to Unemployment Insurance: The Role of Heterogeneity","year":2023,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Unemployment; Receipt; Matching (statistics); Incentive; Scope (computer science); Differential (mechanical device)","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":[0.001231967,0.001161251,0.0008487161,0.002892361,0.0006610486,0.00177495,0.002043243,0.001847393,0.06040324],"category_scores_gemma":[0.007409218,0.0005455241,0.000749294,0.006130487,0.0003478545,0.0008370688,0.00138207,0.001643608,0.07226849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001598448,"about_ca_system_score_gemma":0.001856215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06763919,"about_ca_topic_score_gemma":0.09680529,"domain_scores_codex":[0.9988964,0.0001869638,0.0001718124,0.0002384351,0.0003168198,0.0001895626],"domain_scores_gemma":[0.9962491,0.0009018424,0.0007630239,0.0007161728,0.001069065,0.0003007341],"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":[0.00005888521,0.00003058338,0.002492696,0.0002561963,0.00001584949,0.00002014479,0.00003706049,0.0003368151,0.00006617991,0.0005589229,0.9942375,0.001889099],"study_design_scores_gemma":[0.0003556428,0.0000290677,0.02713857,0.000296414,0.00001958486,0.00006725812,0.0002412008,0.001006687,0.000317806,0.001161255,0.9693229,0.00004352449],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003147113,0.00002777748,0.00004521389,0.00007562022,0.0000125807,0.00001089694,0.9988166,0.00008510809,0.0006114123],"genre_scores_gemma":[0.0009144736,0.00003260058,0.0002373778,0.00004278421,0.000007462456,0.00008444228,0.997203,0.00003374566,0.001444196],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06763919,"threshold_uncertainty_score":0.2020689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09345726339994374,"score_gpt":0.368101731073611,"score_spread":0.2746444676736672,"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."}}