{"id":"W4206003048","doi":"10.7202/1084739ar","title":"Archival Interventions and Disentangling Legacy Records","year":2022,"lang":"en","type":"article","venue":"Archivaria","topic":"Digital and Traditional Archives Management","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université Laval; Université du Québec à Montréal","funders":"","keywords":"Discoverability; Records management; Government (linguistics); Library science; National archives; Psychological intervention; Historical record; Archival science; Function (biology); Electronic records; Political science; Genealogy; History; Public administration; Public relations; World Wide Web; Law; Computer science; Medicine; Biography; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.02333565,0.0006040101,0.0004868634,0.003784918,0.02799485,0.01160404,0.004861457,0.001917104,0.006358401],"category_scores_gemma":[0.05982637,0.000532845,0.0003538384,0.003471509,0.02404431,0.009739553,0.01451557,0.003692379,0.0005647802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01532556,"about_ca_system_score_gemma":0.03627182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06266503,"about_ca_topic_score_gemma":0.1221557,"domain_scores_codex":[0.9753185,0.01809296,0.0007063545,0.001400933,0.002307515,0.00217378],"domain_scores_gemma":[0.9516935,0.02994322,0.00448139,0.007622675,0.004120598,0.002138525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007884498,0.0004013253,0.007214796,0.0003218727,0.00001645857,0.0005977207,0.7654201,0.000213329,0.0006755471,0.07499034,0.004341968,0.1457277],"study_design_scores_gemma":[0.00003285118,0.0002007746,0.006981349,0.0009329283,0.00004576948,0.0002911699,0.8021649,0.0003851485,0.00178009,0.0256745,0.1614729,0.00003776128],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7619269,0.005173449,0.02476143,0.02745281,0.0004512874,0.00116374,0.0001304421,0.0003691916,0.1785707],"genre_scores_gemma":[0.9689552,0.001817222,0.01250061,0.0009921995,0.00006934801,0.0003796067,0.00004945484,0.00005225784,0.01518414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06266503,"threshold_uncertainty_score":0.1246006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04559966970172311,"score_gpt":0.237339328504404,"score_spread":0.1917396588026808,"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."}}