{"id":"W2626429959","doi":"10.15402/esj.v2i2.166","title":"Using Oral History to Assess Community Impact: A Conversation with Beverly C. Tyler, Historian, Three Village Historical Society","year":2017,"lang":"en","type":"article","venue":"Engaged Scholar Journal Community-Engaged Research Teaching and Learning","topic":"Oral History, Memory, Narrative Analysis","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conversation; Oral history; Narrative; Context (archaeology); Exhibition; Sociology; History; Asset (computer security); Media studies; Library science; Visual arts; Art history; Anthropology; Art; Literature; Archaeology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5467306,0.0005487947,0.0008635774,0.0008393028,0.5793813,0.002610936,0.001760926,0.0002399292,0.0005630531],"category_scores_gemma":[0.1807169,0.0004753285,0.0004215008,0.0001328949,0.001160442,0.002893192,0.00060532,0.5223604,0.00003054905],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01040674,"about_ca_system_score_gemma":0.0006496905,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03713454,"about_ca_topic_score_gemma":0.007830469,"domain_scores_codex":[0.5457499,0.4519083,0.0004238359,0.0002526107,0.001011254,0.0006541426],"domain_scores_gemma":[0.9526627,0.04305387,0.0006727586,0.001506574,0.001296391,0.000807682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002817405,0.0003275804,0.002885807,0.00007831821,0.0004126619,0.00004712436,0.9760475,0.0004127517,0.007041984,0.00005836732,0.007738588,0.004667647],"study_design_scores_gemma":[0.001430736,0.001436792,0.003862154,0.0003012934,0.0001531426,0.00007171666,0.7446223,0.001021809,0.00002095229,0.00007055241,0.2464013,0.0006073183],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884806,0.001092643,0.001713657,0.0005414212,0.000835205,0.0003286796,0.000006895077,0.0001524441,0.006848463],"genre_scores_gemma":[0.9881293,0.00004684434,0.00378298,0.0001396229,0.0008722598,0.00001158546,0.00001965842,0.0001268454,0.006870891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5782209,"threshold_uncertainty_score":0.9997699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4553317529368703,"score_gpt":0.4101680638194075,"score_spread":0.04516368911746288,"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."}}