{"id":"W4404230625","doi":"10.3998/nasig.6733","title":"Metadata for Everyone: Identifying Metadata Quality Issues Across Cultures","year":2024,"lang":"en","type":"article","venue":"NASIG Proceedings","topic":"Digital and Traditional Archives Management","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Council of University Libraries; University of Toronto","funders":"","keywords":"Metadata; Meta Data Services; World Wide Web; Metadata repository; Geospatial metadata; Computer science; Database catalog; Data element; Quality (philosophy); Information retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003661791,0.0002040753,0.0002272486,0.000075464,0.0004142777,0.006292402,0.0003816405,0.00001838294,0.0001065965],"category_scores_gemma":[0.00007074523,0.000155678,0.0001903938,0.00006731434,0.0001770018,0.005486484,0.0001958201,0.0001209257,0.00009234725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002392305,"about_ca_system_score_gemma":0.00001344671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001046591,"about_ca_topic_score_gemma":0.00004606949,"domain_scores_codex":[0.9985278,0.000005053037,0.0003189657,0.0004663889,0.0003519927,0.0003298042],"domain_scores_gemma":[0.9995632,0.00008058431,0.00006414029,0.0001066217,0.0001187103,0.00006676291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002262524,0.00005310573,0.00001013888,0.0007936704,0.0002426359,0.000002494181,0.009838366,2.102607e-7,0.0001408474,0.8607907,0.1238549,0.00425029],"study_design_scores_gemma":[0.000129635,0.00004502989,0.0005929036,0.0001115511,0.00007630908,0.000002571075,0.00392072,0.00003100705,0.0004953583,0.1053929,0.8889739,0.000228195],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.166252,0.01933017,0.005777566,0.01184017,0.00734969,0.003888678,0.0128072,0.003266443,0.7694881],"genre_scores_gemma":[0.7268586,0.00007802108,0.000554033,0.0004259567,0.001329001,0.0001693891,0.0004135779,0.00003599042,0.2701355],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.765119,"threshold_uncertainty_score":0.9947392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1478028041162673,"score_gpt":0.3819531090440895,"score_spread":0.2341503049278222,"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."}}