{"id":"W19324623","doi":"10.1098/rsbl.2009.0094","title":"Digital Technologies in Higher Education: Sweeping Expectations and Actual Effects","year":2011,"lang":"en","type":"book","venue":"Biology Letters","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Competitor analysis; Higher education; Politics; Emerging technologies; Public relations; Political science; Business; Engineering; Marketing; Economic growth; Economics; Computer science","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":[0.001153867,0.0001892616,0.000162101,0.0005000245,0.0009863154,0.003248774,0.000509249,0.0005938347,0.0136161],"category_scores_gemma":[0.007316597,0.0001003097,0.0001287352,0.001335718,0.002125745,0.00185377,0.0008204487,0.000726509,0.0004588768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008290532,"about_ca_system_score_gemma":0.003056128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09603863,"about_ca_topic_score_gemma":0.1578582,"domain_scores_codex":[0.9995609,0.0001346578,0.00001129845,0.00004914577,0.0001895285,0.00005433994],"domain_scores_gemma":[0.9952505,0.003621937,0.000249038,0.0001660163,0.0003747888,0.0003377619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000221109,0.0001220559,0.1643887,0.0003192453,0.00003556305,0.0005915475,0.009354958,0.01934161,0.001113368,0.3675573,0.02872247,0.408232],"study_design_scores_gemma":[0.00008981639,0.000415845,0.403982,0.0009498905,0.0001263435,0.0007343605,0.02546361,0.03925361,0.002263532,0.2681754,0.2584506,0.00009510724],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.5006042,0.0062076,0.006701999,0.02833068,0.0001204324,0.00002818701,0.0005559102,0.0000900304,0.4573609],"genre_scores_gemma":[0.9848804,0.001635623,0.000597934,0.0003366319,0.00002169789,0.00000882115,0.00006979386,0.00001208146,0.01243698],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09603863,"threshold_uncertainty_score":0.1909593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01688041786125355,"score_gpt":0.2522487709032578,"score_spread":0.2353683530420043,"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."}}