{"id":"W4415522343","doi":"10.22363/2313-1683-2024-21-2-657-682","title":"Ethical Aspects of Measuring Intelligence: Towards Competence and Fairness","year":2024,"lang":"en","type":"article","venue":"RUDN Journal of Psychology and Pedagogics","topic":"Cognitive Abilities and Testing","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Russian Science Foundation; Yale University; University of Cambridge; McGill University; University of Oxford; American Educational Research Association","keywords":"Operationalization; Competence (human resources); Human intelligence; Ethical issues; Variety (cybernetics); Ethical standards; Context (archaeology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001381863,0.0001315608,0.0003163804,0.0001624494,0.00006284197,0.00002874149,0.0001363079,0.0002564021,0.0004010174],"category_scores_gemma":[0.0002992589,0.0001060171,0.00008087902,0.0001581198,0.0004534154,0.00006962124,0.00004892188,0.001031277,0.000006095797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008291205,"about_ca_system_score_gemma":0.00006982997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001589217,"about_ca_topic_score_gemma":0.000006433777,"domain_scores_codex":[0.998687,0.0002251523,0.0004964079,0.0002274989,0.0001611574,0.0002027883],"domain_scores_gemma":[0.9986328,0.0007670152,0.0001614634,0.0001169656,0.0002149007,0.0001068254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003502349,0.0001706331,0.01441684,0.0003343748,0.0003324848,0.0008042197,0.01437907,0.000005886108,0.001655761,0.3390386,0.0003042807,0.6282077],"study_design_scores_gemma":[0.00169445,0.004338242,0.4368294,0.001763388,0.0004310373,0.01968399,0.01818323,0.0002571707,0.0009432997,0.4952943,0.01983108,0.0007504278],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.919521,0.02534413,0.01336189,0.003628596,0.001895275,0.00008469912,0.0000101631,0.0000244553,0.03612978],"genre_scores_gemma":[0.9977831,0.0008873084,0.0006026113,0.0003687805,0.0002520417,0.000001266112,4.632805e-7,0.00001129862,0.00009311755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6274573,"threshold_uncertainty_score":0.4480443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1703624077064932,"score_gpt":0.4399477973933194,"score_spread":0.2695853896868262,"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."}}