{"id":"W1622445028","doi":"10.5703/1288284315628","title":"The Punishment for Dreamers: Big Data, Retention, and Academic Libraries","year":2015,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Purdue Pharma (Canada)","funders":"","keywords":"Punishment (psychology); Data retention; Computer science; Big data; Internet privacy; Computer security; Psychology; Operating system; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.02454093,0.0003794654,0.001004742,0.00229204,0.01198219,0.01785297,0.002190446,0.007583135,0.01935106],"category_scores_gemma":[0.105368,0.0004292778,0.0006363243,0.005106704,0.02928271,0.03052301,0.01171127,0.01319395,0.00205011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006618741,"about_ca_system_score_gemma":0.01471099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01628479,"about_ca_topic_score_gemma":0.02169068,"domain_scores_codex":[0.9837885,0.01129779,0.0004184882,0.0006638472,0.002321321,0.001510093],"domain_scores_gemma":[0.8887561,0.06558945,0.01365483,0.00639749,0.01021553,0.0153867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005718693,0.000284529,0.01617009,0.0003823111,0.0001182779,0.0002713504,0.008657636,0.0009312565,0.00007020751,0.5465287,0.3436286,0.08238515],"study_design_scores_gemma":[0.0001760208,0.0001316606,0.00628,0.001018858,0.00005299533,0.000220195,0.02779788,0.001979482,0.0002442822,0.8392726,0.122678,0.0001481019],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02302924,0.007977207,0.002898528,0.9286225,0.00105336,0.00001970183,0.000166722,0.00006234481,0.03617043],"genre_scores_gemma":[0.8545179,0.008369126,0.001958703,0.1135603,0.003586222,0.00009460111,0.0001173016,0.0001275791,0.01766824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.982147,"threshold_uncertainty_score":0.1297864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7045384724497639,"score_gpt":0.440208832944228,"score_spread":0.2643296395055359,"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."}}