{"id":"W4318712074","doi":"10.2139/ssrn.4328776","title":"Marrying Your Job: Matching and Mobility with Geographic Heterogeneity","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Matching (statistics); Geographic mobility; Geography; Occupational mobility; Computer science; Demographic economics; Population; Economics; Demography; Sociology; Statistics; Mathematics","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":[0.01312646,0.0004374668,0.001611318,0.00273421,0.001963874,0.002948906,0.002534033,0.002843526,0.007249835],"category_scores_gemma":[0.06611923,0.0005584911,0.001804182,0.005109658,0.002318423,0.00389108,0.003085928,0.001830973,0.0007841707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107912,"about_ca_system_score_gemma":0.001223456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02479946,"about_ca_topic_score_gemma":0.01860143,"domain_scores_codex":[0.9893686,0.007722928,0.0004074131,0.00130808,0.000345295,0.0008476542],"domain_scores_gemma":[0.9364074,0.05154216,0.005578856,0.00379921,0.0009423118,0.001729983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009169524,0.0004945054,0.9155601,0.0001328939,0.001345722,0.0005116977,0.001670885,0.02990416,0.0002368621,0.01880293,0.001768263,0.02865507],"study_design_scores_gemma":[0.0002432114,0.0005843687,0.475078,0.0001618073,0.001270216,0.0007015684,0.01108299,0.3973646,0.000350573,0.11053,0.002470939,0.0001618076],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757426,0.0005635443,0.02024237,0.001470484,0.00004674935,0.00008493112,0.0004805679,0.00003653371,0.001332271],"genre_scores_gemma":[0.9975218,0.00009977614,0.001314354,0.00003988033,0.00003034972,0.00003303895,0.0001768283,0.000006302721,0.0007776493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02479946,"threshold_uncertainty_score":0.06942022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641571200652229,"score_gpt":0.2928684347802457,"score_spread":0.2764527227737234,"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."}}