{"id":"W4236648382","doi":"10.18653/v1/w17-22","title":"Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature","year":2017,"lang":"en","type":"paratext","venue":"","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Joint (building); Linguistics; Digital humanities; Computer science; Cultural heritage; Computational linguistics; Sociology; Humanities; Library science; Natural language processing; History; Art; Philosophy; Engineering; Architectural engineering; Archaeology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01062837,0.001107143,0.001593616,0.003330972,0.003449741,0.01144182,0.002314772,0.001960293,0.1018806],"category_scores_gemma":[0.01903318,0.0006176847,0.001184088,0.003415321,0.003330739,0.01271023,0.009290515,0.00567469,0.04066646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002761814,"about_ca_system_score_gemma":0.008762117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00629802,"about_ca_topic_score_gemma":0.01965781,"domain_scores_codex":[0.9947571,0.003067526,0.0002612804,0.000603676,0.001006743,0.0003037176],"domain_scores_gemma":[0.9804776,0.008948104,0.0003170796,0.003262823,0.003603018,0.003391352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001267612,0.0001450623,0.0003613892,0.0003139334,0.00003514716,0.0001112222,0.001087365,0.0003269151,0.0007291406,0.01576046,0.8853068,0.09569589],"study_design_scores_gemma":[0.00002199808,0.00002049745,0.0005649807,0.0002454971,0.00001829346,0.0001009755,0.0009760287,0.001187141,0.0009328586,0.02607228,0.969836,0.000023534],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02281507,0.0348989,0.2914546,0.1641696,0.1175657,0.001128125,0.02692826,0.01638317,0.3246565],"genre_scores_gemma":[0.05790085,0.01844176,0.08645338,0.008093189,0.01738553,0.001116685,0.0592281,0.01124097,0.7401394],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1018806,"threshold_uncertainty_score":0.3408245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1544260734822731,"score_gpt":0.313592085202904,"score_spread":0.1591660117206309,"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."}}