{"id":"W2097026038","doi":"10.18438/b89c70","title":"Looking for Links: How Faculty Research Productivity Correlates with Library Investment and Why Electronic Library Materials Matter Most","year":2015,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Operationalization; Productivity; Investment (military); Institution; Variety (cybernetics); Scholarly communication; Ordinary least squares; Public relations; Computer science; Marketing; Sociology; Business; Economics; Political science; Social science; Econometrics; Publishing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.005297478,0.0002767958,0.0003982336,0.003314193,0.0008150366,0.006605226,0.0009323108,0.001075864,0.01776081],"category_scores_gemma":[0.05862539,0.0002513018,0.0008301858,0.0075511,0.001284634,0.005393797,0.001939324,0.000808278,0.003153988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001671867,"about_ca_system_score_gemma":0.001766927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006652443,"about_ca_topic_score_gemma":0.005489342,"domain_scores_codex":[0.9958265,0.001674634,0.00036171,0.0006725756,0.0008607723,0.0006037058],"domain_scores_gemma":[0.9152089,0.04944508,0.02377309,0.00311265,0.005052065,0.003408281],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008815991,0.0001599936,0.9756032,0.00004098784,0.0001373664,0.00007807117,0.0008234482,0.0003785451,0.0001084627,0.001129832,0.000769138,0.0206829],"study_design_scores_gemma":[0.00001403343,0.0001236883,0.9863848,0.0001100979,0.0002009639,0.0001116687,0.005435145,0.001856594,0.0006765001,0.002289666,0.002776097,0.00002070641],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865614,0.0008177562,0.001121125,0.002085467,0.00002470539,0.00001609804,0.0003555003,0.00004019983,0.008977778],"genre_scores_gemma":[0.9973137,0.0002368255,0.0002789807,0.000152071,0.00004557542,0.00001021261,0.0002494136,0.00001912789,0.001694175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9966858,"threshold_uncertainty_score":0.05941582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3275103745388362,"score_gpt":0.4735892109220031,"score_spread":0.1460788363831669,"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."}}