{"id":"W4385726626","doi":"10.32920/23929158.v1","title":"Getting Left Behind: Who gained and who didn’t in an improving labour market","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Precarity; Poverty; Left behind; Political science; Economic growth; Sociology; Economics; Gender studies; Psychology","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":[0.004278668,0.0002958823,0.0007083464,0.001037941,0.005720019,0.005946459,0.001228775,0.001895378,0.01395434],"category_scores_gemma":[0.009292284,0.0002295671,0.0004890085,0.001471234,0.005288445,0.009971305,0.005160394,0.005194816,0.002249007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009145968,"about_ca_system_score_gemma":0.02202787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2604042,"about_ca_topic_score_gemma":0.4428875,"domain_scores_codex":[0.995758,0.0005336659,0.0001080473,0.0001849706,0.0005761941,0.002839168],"domain_scores_gemma":[0.9920941,0.0003160291,0.0005755579,0.0001912938,0.001164731,0.005658253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007029721,0.001377289,0.2973398,0.0006582226,0.0001002057,0.001274783,0.1329329,0.0002247161,0.001255044,0.02707635,0.1792004,0.3578574],"study_design_scores_gemma":[0.0001883857,0.0006772534,0.431484,0.002322673,0.0001531868,0.0004761827,0.3556962,0.0003921229,0.0006556467,0.01389096,0.1938842,0.00017911],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6816742,0.009068758,0.0003272182,0.2459237,0.002433827,0.0001616761,0.001574909,0.00005263085,0.05878303],"genre_scores_gemma":[0.9445768,0.004495133,0.0003934175,0.01764527,0.0002791456,0.00008938467,0.0006443708,0.0000657004,0.0318108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2604042,"threshold_uncertainty_score":0.517777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06291610227778176,"score_gpt":0.403332860732383,"score_spread":0.3404167584546012,"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."}}