{"id":"W4385275429","doi":"10.1016/j.jeca.2023.e00321","title":"How does pre-school attendance affect school performance? An application of Gini-BMA methodology on PISA 2018 dataset","year":2023,"lang":"en","type":"article","venue":"The Journal of Economic Asymmetries","topic":"Intergenerational and Educational Inequality Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Affect (linguistics); Attendance; Mathematics education; Psychology; Statistics; Econometrics; Economics; Mathematics; Economic growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002887105,0.0007360665,0.0009841261,0.002648554,0.0007358172,0.001634351,0.002076466,0.001845598,0.01113257],"category_scores_gemma":[0.0130313,0.0003591772,0.001383852,0.004168542,0.0003535612,0.00086543,0.001552615,0.001898437,0.008361656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016696,"about_ca_system_score_gemma":0.001196297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07839858,"about_ca_topic_score_gemma":0.07879059,"domain_scores_codex":[0.9984998,0.0005458763,0.00008697432,0.0003987302,0.0002548625,0.0002137868],"domain_scores_gemma":[0.9960331,0.001743794,0.0005508625,0.0007103743,0.0007022187,0.0002596852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0007107382,0.0005356194,0.2630771,0.0004342315,0.001115022,0.0002684837,0.0002112655,0.01483555,0.000275456,0.002532722,0.6898614,0.0261425],"study_design_scores_gemma":[0.000679886,0.0001630127,0.7231675,0.0002958642,0.0003990106,0.0003032514,0.001372752,0.06633269,0.0008145967,0.003791781,0.2025393,0.0001403132],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1315304,0.0005624646,0.002006894,0.001967841,0.0002538541,0.00007765777,0.8593261,0.0009544853,0.003320306],"genre_scores_gemma":[0.1159344,0.0001166416,0.002643372,0.000203398,0.00008933093,0.0001831491,0.8778695,0.0001499543,0.002810251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07839858,"threshold_uncertainty_score":0.1558845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1203184343296714,"score_gpt":0.4016228610986814,"score_spread":0.28130442676901,"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."}}