{"id":"W4390760903","doi":"10.1177/20539517231224247","title":"A feeling for the algorithm: Diversity, expertise, and artificial intelligence","year":2024,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Social Sciences and Humanities Research Council","keywords":"Diversity (politics); Sociology; Epistemology; Normative; Set (abstract data type); Computer science; CLARITY; Feeling; Artificial intelligence; Social psychology; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01137555,0.0003861554,0.0003942914,0.001628425,0.00521248,0.008351295,0.001048183,0.004231377,0.00412048],"category_scores_gemma":[0.02290885,0.0002964635,0.0005047777,0.001110552,0.06185933,0.01838757,0.006316618,0.006004885,0.0006336301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003231197,"about_ca_system_score_gemma":0.001880561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001904234,"about_ca_topic_score_gemma":0.001139954,"domain_scores_codex":[0.9892721,0.007720297,0.0002485426,0.0009223178,0.00136733,0.0004694464],"domain_scores_gemma":[0.980432,0.0147716,0.00106029,0.001578937,0.001182054,0.0009749842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001950253,0.00001548727,0.0009088746,0.00007330385,0.0000114272,0.00007162668,0.01475064,0.0006604707,0.0002306977,0.9640034,0.00462338,0.01463124],"study_design_scores_gemma":[0.00001444163,0.00001788783,0.0004932781,0.0001189306,0.000005653137,0.000132154,0.004618414,0.001145402,0.0001286936,0.9557255,0.03758245,0.00001720966],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.09683333,0.01455218,0.2005108,0.3606932,0.001319231,0.00008300908,0.0000956488,0.0002098868,0.3257028],"genre_scores_gemma":[0.9590507,0.002201267,0.02241341,0.009621277,0.0005456223,0.00007401037,0.00002643788,0.00008301663,0.005984222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9947875,"threshold_uncertainty_score":0.06016034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4130394589546723,"score_gpt":0.4336775183036176,"score_spread":0.02063805934894525,"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."}}