{"id":"W3120412237","doi":"10.18438/eblip29736","title":"Research Productivity and Its Relationship to Library Collections","year":2020,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Productivity; Data collection; Collection development; Computer science; Resource (disambiguation); Regression analysis; Library science; Statistics; Economics; Mathematics","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":[0.01167711,0.0004294444,0.0005155469,0.008137038,0.000985296,0.00548968,0.001408037,0.0003566221,0.01292205],"category_scores_gemma":[0.09708827,0.0003874246,0.0006917406,0.02210655,0.001306385,0.004034192,0.003172259,0.0008370741,0.002807692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003145267,"about_ca_system_score_gemma":0.004675459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006920577,"about_ca_topic_score_gemma":0.00516663,"domain_scores_codex":[0.9823357,0.005633668,0.002550519,0.001440215,0.007096579,0.0009432035],"domain_scores_gemma":[0.7958961,0.1060585,0.05673891,0.009044496,0.02528573,0.0069763],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002531911,0.0003190906,0.8707516,0.0006174031,0.0004145645,0.0002523893,0.001278654,0.002252667,0.000779601,0.002109873,0.005293141,0.115678],"study_design_scores_gemma":[0.00001540176,0.0001434724,0.9811644,0.0001191062,0.0000829267,0.0003465518,0.001740521,0.001105904,0.001073344,0.001118549,0.01305665,0.00003315905],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325987,0.004674473,0.005727153,0.003838598,0.0001201353,0.0001761663,0.005339107,0.000372534,0.047153],"genre_scores_gemma":[0.9870211,0.001989343,0.00325525,0.0001670138,0.0003292936,0.0001075814,0.002270618,0.00008014038,0.004779533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.991863,"threshold_uncertainty_score":0.06175518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5682970782440521,"score_gpt":0.5375281748841486,"score_spread":0.03076890335990345,"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."}}