{"id":"W2332943239","doi":"10.3366/ijhac.2016.0156","title":"Mindset and Guidelines: Insights to Enhance Collaborative, Campus-wide, Cross-sectoral Digital Humanities Initiatives","year":2016,"lang":"en","type":"article","venue":"International Journal of Humanities and Arts Computing","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mindset; Dichotomy; Digital humanities; Scholarship; Government (linguistics); Political science; Sociology; Public relations; Social science; Humanities; Library science; Epistemology; Computer science; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.04398666,0.0007030061,0.0005772501,0.005110867,0.01034472,0.01934328,0.004342568,0.005179511,0.009807917],"category_scores_gemma":[0.08192612,0.0005440112,0.0006296155,0.005220728,0.01551514,0.01662679,0.01612584,0.006778333,0.002468825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01300221,"about_ca_system_score_gemma":0.05225494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01876561,"about_ca_topic_score_gemma":0.06886456,"domain_scores_codex":[0.9526409,0.0368163,0.001369887,0.001549836,0.005325465,0.00229756],"domain_scores_gemma":[0.9202861,0.04459127,0.002752419,0.007324449,0.0116998,0.01334598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003706463,0.0005891037,0.004385933,0.0006347278,0.00001941484,0.001745345,0.4050728,0.0007757308,0.00172688,0.3518111,0.06137409,0.1718278],"study_design_scores_gemma":[0.00004046967,0.0001060242,0.001443647,0.001115877,0.00001537696,0.0004094822,0.2623907,0.001530309,0.0007753004,0.1617536,0.5703608,0.00005833583],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0942944,0.002384218,0.2257993,0.1676826,0.001640521,0.003289191,0.0005105075,0.002087907,0.5023113],"genre_scores_gemma":[0.4665138,0.0019353,0.4745817,0.01082199,0.0002213452,0.00232234,0.0007526681,0.0007924712,0.0420583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04398666,"threshold_uncertainty_score":0.2326265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06047957867997,"score_gpt":0.3216382304432769,"score_spread":0.2611586517633069,"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."}}