{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05704672,0.0004798775,0.001070596,0.01131697,0.01012763,0.01679133,0.001625265,0.001871742,0.002580021],"category_scores_gemma":[0.1683545,0.0005856401,0.0007589763,0.01583582,0.01483803,0.02601121,0.01760127,0.002757706,0.0003206395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01045522,"about_ca_system_score_gemma":0.01055215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02717555,"about_ca_topic_score_gemma":0.03053379,"domain_scores_codex":[0.9534138,0.02569194,0.005716735,0.003304392,0.009053086,0.00281985],"domain_scores_gemma":[0.8371092,0.07658417,0.02401857,0.02480006,0.03430797,0.00317996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001369086,0.00008753926,0.2151195,0.0005226851,0.0000978776,0.0002624685,0.6095243,0.0001700902,0.001159683,0.08272344,0.001332475,0.08886299],"study_design_scores_gemma":[0.00001165082,0.0001083728,0.0567227,0.001273229,0.0001380759,0.0003018553,0.8847768,0.0007163291,0.002656351,0.0344557,0.01874825,0.00009061585],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9173406,0.003008023,0.02537927,0.008309295,0.0001428542,0.0005187515,0.0005877457,0.0000689377,0.04464463],"genre_scores_gemma":[0.9879126,0.0007152313,0.009264609,0.0005461739,0.00003469427,0.0002462008,0.0001526392,0.00004478589,0.001082968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05704672,"threshold_uncertainty_score":0.3016955,"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."}}