{"id":"W2394849579","doi":"10.29173/cais32","title":"In Search of Taxonomies: The Relevance of Cognitive Reading Models in the Design of Electronic Text in Literary Studies","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hypertext; Hypermedia; Relevance (law); Reading (process); Exploit; Computer science; Cognition; Cognitive computing; World Wide Web; Cognitive science; Literature; Psychology; Linguistics; Art; Philosophy; Political 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01204975,0.0007978019,0.0007744705,0.00585667,0.005135268,0.01732917,0.001715907,0.003475348,0.009007243],"category_scores_gemma":[0.03570817,0.0005862134,0.001100364,0.005270581,0.01682195,0.04228205,0.004891778,0.004576548,0.001226791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005085723,"about_ca_system_score_gemma":0.004621531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005007965,"about_ca_topic_score_gemma":0.01228691,"domain_scores_codex":[0.9919599,0.006256474,0.0003292407,0.0007086931,0.0005666327,0.0001791086],"domain_scores_gemma":[0.9718162,0.02131206,0.001665692,0.002083339,0.002161135,0.0009614733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008703807,0.00007344167,0.002586226,0.0003142541,0.00002439271,0.00009967788,0.0391123,0.0006252445,0.0004398708,0.9120879,0.006029227,0.03852043],"study_design_scores_gemma":[0.00004334014,0.00005381185,0.001608341,0.0004857202,0.00003799549,0.0001620289,0.04123237,0.00739431,0.0003919945,0.9112691,0.03728823,0.00003276253],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1547582,0.01356987,0.4927684,0.1315769,0.001925043,0.0004441128,0.0004863342,0.0007253888,0.2037458],"genre_scores_gemma":[0.7513093,0.003939787,0.2283318,0.003868928,0.0005510139,0.0005363359,0.0005553626,0.0003297696,0.01057779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01732917,"threshold_uncertainty_score":0.06372595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08248498245324194,"score_gpt":0.2706388923331601,"score_spread":0.1881539098799181,"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."}}