{"id":"W4367313592","doi":"10.1145/3594728","title":"Positioning Paradata: A Conceptual Frame for AI Processual Documentation in Archives and Recordkeeping Contexts","year":2023,"lang":"en","type":"article","venue":"Journal on Computing and Cultural Heritage","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; University of British Columbia","keywords":"Documentation; Computer science; Transparency (behavior); Process (computing); Accountability; Metadata; Agency (philosophy); Workflow; Scope (computer science); Scholarship; Data science; Sociology; World Wide Web; Political science; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003633979,0.0001189167,0.0001658417,0.0001074152,0.0005002616,0.0007371373,0.0001799248,0.00003833736,0.00000153308],"category_scores_gemma":[0.0000981671,0.0001011207,0.00003386663,0.0002502814,0.00007479941,0.0009865215,0.00008858686,0.0002673374,0.000004482166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002699507,"about_ca_system_score_gemma":0.00002825699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001494284,"about_ca_topic_score_gemma":0.00001551955,"domain_scores_codex":[0.9989079,0.00008282121,0.0003066521,0.0002659835,0.00014016,0.0002965299],"domain_scores_gemma":[0.9993146,0.0003368051,0.0001310201,0.00006702932,0.0000584274,0.0000921195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000287145,0.0001573199,0.005207957,0.000218194,0.00008001738,0.0003257596,0.1157718,0.006433691,0.01676117,0.1968399,0.001376462,0.6565406],"study_design_scores_gemma":[0.002074377,0.001886651,0.01458421,0.001794479,0.00001759781,0.0006219801,0.03591949,0.8649911,0.005754203,0.06866118,0.002798936,0.00089574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9281251,0.0002518869,0.06887148,0.002082388,0.0002694732,0.0001955533,0.000002005156,0.00008802573,0.0001140601],"genre_scores_gemma":[0.9909835,0.00008508307,0.008023314,0.0006260828,0.0001603244,0.000005955958,0.000004967305,0.000006503554,0.0001042563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8585575,"threshold_uncertainty_score":0.7108232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03338625850983379,"score_gpt":0.340418111546091,"score_spread":0.3070318530362572,"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."}}