{"id":"W4396543036","doi":"10.17141/iconos.79.2024.5911","title":"Desajuste educativo y ajuste económico: ¿cómo respondió el mercado de trabajo mexicano ante la pandemia?","year":2024,"lang":"es","type":"article","venue":"Íconos - Revista de Ciencias Sociales","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Demographic economics; Closure (psychology); Pandemic; Population; Educational attainment; Working population; Coronavirus disease 2019 (COVID-19); Socioeconomic status; Demography; Economics; Geography; Economic growth; Sociology; Medicine; Disease","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005409324,0.0008629121,0.001341708,0.0005006814,0.002824212,0.000641158,0.0008532283,0.001005153,0.0009786056],"category_scores_gemma":[0.001791446,0.0008139635,0.0007214032,0.001142449,0.001507713,0.0005075745,0.0004421367,0.002004252,0.000690416],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002234797,"about_ca_system_score_gemma":0.008782666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001064508,"about_ca_topic_score_gemma":0.0003547695,"domain_scores_codex":[0.9913439,0.003139629,0.00156221,0.001224884,0.0005642921,0.002165113],"domain_scores_gemma":[0.9935482,0.004338718,0.0005521259,0.000661594,0.000308965,0.0005903763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001868637,0.0003993366,0.2662042,0.006424081,0.00104042,0.0002544259,0.0821689,0.000002561544,0.002449414,0.4702964,0.1515194,0.01905402],"study_design_scores_gemma":[0.00105544,0.0003047932,0.1687432,0.007023024,0.001408955,0.00005130878,0.04024914,0.0001384731,0.0002455683,0.01148905,0.7676554,0.001635654],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.878932,0.05175325,0.0002216701,0.02014139,0.001964102,0.001741043,0.0006207496,0.0006368099,0.04398901],"genre_scores_gemma":[0.9617554,0.01459105,0.0002276082,0.002431971,0.002997517,0.0003044541,0.00004640709,0.0001913153,0.0174543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.616136,"threshold_uncertainty_score":0.9999346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0810259284687996,"score_gpt":0.43154116884854,"score_spread":0.3505152403797405,"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."}}