{"id":"W3194032252","doi":"10.14288/1.0401273","title":"Becoming engineers: how students leverage relationships between documents and learning activities","year":2021,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Leverage (statistics); Computer science; Mathematics education; Data science; Knowledge management; World Wide Web; Artificial intelligence; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003365147,0.0003937492,0.0003443988,0.001239995,0.002547903,0.00972396,0.0009875966,0.001086061,0.002068241],"category_scores_gemma":[0.01468782,0.0003996988,0.0002860651,0.0006037828,0.00216109,0.00361948,0.004627958,0.001487407,0.0005566449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008787969,"about_ca_system_score_gemma":0.002297997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002382505,"about_ca_topic_score_gemma":0.00523671,"domain_scores_codex":[0.9960335,0.002008696,0.0001596046,0.0003784834,0.0009453228,0.0004743935],"domain_scores_gemma":[0.9923083,0.003381812,0.001371435,0.0003954005,0.0007323645,0.001810674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005594119,0.0008610223,0.1450426,0.0001313581,0.00002646795,0.001403245,0.7655274,0.00011199,0.005238686,0.001460172,0.001304312,0.07883681],"study_design_scores_gemma":[0.00001535598,0.0004786717,0.07774975,0.0001081047,0.00004010305,0.001264475,0.8908613,0.0004124914,0.001727175,0.001502746,0.0257837,0.00005617326],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949876,0.0001293295,0.0007745926,0.0004066075,0.000009695274,0.00002265935,0.000008001138,0.00001546593,0.003646058],"genre_scores_gemma":[0.9961513,0.0002462661,0.001026246,0.0001519029,0.000006141409,0.00001909484,0.00002208262,0.000008996939,0.002367894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.990276,"threshold_uncertainty_score":0.01779681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02072186712020941,"score_gpt":0.2249659799304872,"score_spread":0.2042441128102778,"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."}}