{"id":"W1558250735","doi":"10.16995/dscn.263","title":"CARAT–Computer-Assisted Reading and Analysis of Texts: The Appropriation of a Technology","year":2009,"lang":"en","type":"article","venue":"Digital Studies / Le champ numérique","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Appropriation; Reading (process); Computer science; Cognition; Resistance (ecology); Cognitive science; Data science; Epistemology; Psychology; Linguistics; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001495865,0.0001522249,0.0004512163,0.0002816146,0.000218939,0.0002065109,0.0001614868,0.00004706768,0.000007493725],"category_scores_gemma":[0.00006884414,0.0001046126,0.0001419292,0.0002491242,0.0004887925,0.0004251704,0.00009322551,0.0001002437,8.794181e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002437234,"about_ca_system_score_gemma":0.00001471947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003397033,"about_ca_topic_score_gemma":0.0002957625,"domain_scores_codex":[0.9991055,0.00001953099,0.0003722495,0.0001933982,0.0001452798,0.0001640964],"domain_scores_gemma":[0.999165,0.000105645,0.0002233208,0.0002075783,0.0002766277,0.00002182761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005378549,0.0003316388,0.002172145,0.0001016337,0.002513814,0.000006670784,0.6827106,0.00003709539,0.0000947135,0.2140788,0.0004570905,0.09744204],"study_design_scores_gemma":[0.0004598335,0.0007396913,0.006083625,0.0001434268,0.0000194091,0.000006785145,0.9317203,0.000291467,0.000351323,0.04169675,0.01811381,0.000373626],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9550439,0.001186247,0.0001031022,0.0006903238,0.00006912229,0.0001601488,0.00009660611,0.00005287278,0.04259772],"genre_scores_gemma":[0.998964,0.00008095463,0.000008067033,0.00007380887,0.00008079972,0.00001012903,0.00002931117,0.00000761154,0.0007453155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2490097,"threshold_uncertainty_score":0.4265978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03300535373907686,"score_gpt":0.2438705550559096,"score_spread":0.2108652013168327,"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."}}