{"id":"W21279882","doi":"10.4000/interventionseconomiques.1912","title":"Winners and Losers: Literacy and Enduring Labour Market Inequality in Historical Perspective","year":2013,"lang":"fr","type":"article","venue":"Interventions économiques","topic":"Social Sciences and Governance","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Humanities; Political science; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001276143,0.0001262015,0.0002422141,0.002107438,0.003843175,0.004040412,0.0003752582,0.0008281553,0.007045718],"category_scores_gemma":[0.002981027,0.0001212489,0.0001371136,0.001693009,0.00716972,0.004459249,0.002745476,0.001894999,0.0003254057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004322976,"about_ca_system_score_gemma":0.001208125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04541892,"about_ca_topic_score_gemma":0.08372685,"domain_scores_codex":[0.999225,0.000264098,0.00002791793,0.00009883071,0.00009125,0.0002929528],"domain_scores_gemma":[0.998809,0.0004757973,0.0002294467,0.00005587431,0.0001410425,0.000288795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001576355,0.0000770537,0.08245084,0.0002215397,0.00003110458,0.001243319,0.5068969,0.0001653911,0.0003750131,0.3285016,0.01210677,0.06777287],"study_design_scores_gemma":[0.00001299509,0.0001291379,0.2908133,0.001141247,0.00003769398,0.001153124,0.375562,0.0005071295,0.0004745233,0.06628644,0.2638202,0.00006206058],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7800382,0.01722787,0.0007378149,0.04113712,0.0003713334,0.00001767648,0.000386513,0.0000114004,0.1600721],"genre_scores_gemma":[0.9886587,0.003437932,0.00008469487,0.0005980649,0.0001671327,0.000006481601,0.00005363261,0.000007615272,0.006985765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04541892,"threshold_uncertainty_score":0.09030908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05346494751953398,"score_gpt":0.3520676876719305,"score_spread":0.2986027401523965,"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."}}