{"id":"W3036004323","doi":"10.71781/21084","title":"Essays in dynamic panel data models and labor supply","year":2019,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Panel data; Economics; Data science; Econometrics; Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003288731,0.0009170538,0.001047825,0.001973573,0.0009791343,0.003239624,0.0008802359,0.002443504,0.01641179],"category_scores_gemma":[0.01341568,0.0008027962,0.001604324,0.004825628,0.001806048,0.004704081,0.001835227,0.003481409,0.002857561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002317474,"about_ca_system_score_gemma":0.001501536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004439696,"about_ca_topic_score_gemma":0.004433879,"domain_scores_codex":[0.997979,0.001024694,0.000115845,0.000324939,0.0004544078,0.0001010448],"domain_scores_gemma":[0.9914484,0.007029989,0.0003447677,0.0005429014,0.0004932102,0.0001408103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004877793,0.00006480991,0.001932806,0.0005618049,0.0001557594,0.0002588833,0.000881786,0.01821531,0.0002159789,0.7545478,0.1325589,0.09055739],"study_design_scores_gemma":[0.00001315216,0.00002108967,0.001486685,0.0005369591,0.00003998064,0.0001474424,0.0002528594,0.01032683,0.0001150027,0.7217143,0.2653089,0.00003672758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01325022,0.3011935,0.3129113,0.1497184,0.01005674,0.0001043547,0.003331134,0.0004380154,0.2089964],"genre_scores_gemma":[0.2872746,0.3722935,0.07295225,0.01710861,0.03224865,0.0005676968,0.003871753,0.0009086444,0.2127743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01641179,"threshold_uncertainty_score":0.05490285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026930482179026,"score_gpt":0.291129065433047,"score_spread":0.1884360172151445,"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."}}