{"id":"W4406829136","doi":"10.4236/oalib.1112669","title":"Skill Selection and Productivity Growth","year":2025,"lang":"en","type":"article","venue":"OALib","topic":"Firm Innovation and Growth","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Selection (genetic algorithm); Productivity; Computer science; Economics; Artificial intelligence; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001331733,0.0006515486,0.0004395009,0.002045605,0.0005905433,0.002224086,0.000403266,0.0007868254,0.01045818],"category_scores_gemma":[0.006595905,0.00012746,0.0007115792,0.001620402,0.001269439,0.001431369,0.001625986,0.0009441327,0.001560747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001678345,"about_ca_system_score_gemma":0.001208437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004389412,"about_ca_topic_score_gemma":0.002391676,"domain_scores_codex":[0.9989412,0.0002344747,0.00003549126,0.0001326925,0.0001866939,0.0004693779],"domain_scores_gemma":[0.993305,0.003280949,0.001588157,0.0004334615,0.0005506713,0.0008417292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009660454,0.0008932526,0.4115119,0.0005710177,0.0004679887,0.002785868,0.002816431,0.08122312,0.006633391,0.2370074,0.008465138,0.2466584],"study_design_scores_gemma":[0.0003737045,0.001286355,0.4717017,0.0003495516,0.0002136694,0.001329737,0.004039636,0.08830591,0.005722801,0.3724325,0.05409382,0.0001507064],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8714485,0.002255799,0.02276663,0.003640256,0.00009651915,0.0001520626,0.001254308,0.000289583,0.09809642],"genre_scores_gemma":[0.9927294,0.0005771281,0.0007381099,0.000089981,0.00005941553,0.00002909883,0.0001838121,0.00001228375,0.005580822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01045818,"threshold_uncertainty_score":0.03498608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177679341125626,"score_gpt":0.2052297058285823,"score_spread":0.1934529124173261,"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."}}