{"id":"W3124654604","doi":"","title":"Returns to Human Capital and Investment in New Technology","year":2001,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human capital; Wage; Investment (military); Economics; Labour economics; Market economy","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":[0.0008337494,0.000281955,0.0003116023,0.0009479835,0.00038482,0.001724872,0.0003173281,0.0008417251,0.00859804],"category_scores_gemma":[0.008168184,0.0001482662,0.0001913337,0.001215386,0.001148385,0.001694239,0.0008133817,0.0007984343,0.0007411555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354745,"about_ca_system_score_gemma":0.0005495491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003302171,"about_ca_topic_score_gemma":0.003413739,"domain_scores_codex":[0.999536,0.00009200834,0.00002237846,0.0000562381,0.0001072279,0.0001860542],"domain_scores_gemma":[0.995148,0.002475977,0.001222399,0.0002824582,0.0002954693,0.0005757049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0004272751,0.0004172603,0.2185557,0.0002954896,0.000260596,0.001468805,0.001047974,0.04811158,0.002601743,0.6070414,0.005599611,0.1141727],"study_design_scores_gemma":[0.00008368491,0.0003258504,0.3379223,0.0001892583,0.00008747391,0.0009189692,0.001361066,0.02800922,0.002409763,0.5871501,0.04148107,0.0000613783],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8799031,0.004406741,0.0160638,0.006185243,0.0000966188,0.00009228288,0.001046347,0.0001336669,0.09207226],"genre_scores_gemma":[0.9857413,0.0016322,0.0008407823,0.0001396473,0.0001032359,0.00002314517,0.0002494854,0.00001050342,0.01125968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00859804,"threshold_uncertainty_score":0.02876329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0429560184367102,"score_gpt":0.3040931740208234,"score_spread":0.2611371555841132,"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."}}