{"id":"W1548026587","doi":"10.1002/asi.23601","title":"Estimating open access mandate effectiveness: The <scp>MELIBEA</scp> score","year":2015,"lang":"en","type":"article","venue":"Journal of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal; University of Ottawa","funders":"","keywords":"Mandate; Predictive power; Directory; Actuarial science; Statistics; Predictive value; Medicine; Business; Operations management; Computer science; Mathematics; Economics; Political science; Internal medicine; Law; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication","open_science"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.09250395,0.00007643444,0.0002190015,0.01400927,0.000892889,0.009429511,0.008269386,0.000104579,0.000001640636],"category_scores_gemma":[0.3044939,0.00003527541,0.00006198808,0.08246396,0.0003433074,0.01194067,0.002732312,0.0002841306,0.00001811815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004377884,"about_ca_system_score_gemma":0.001043159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001084343,"about_ca_topic_score_gemma":0.000002965543,"domain_scores_codex":[0.9928706,0.0001289714,0.0007601713,0.0001440091,0.005765649,0.0003306072],"domain_scores_gemma":[0.9748499,0.00412726,0.002438451,0.0003898104,0.01804697,0.0001476136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002523643,0.0000633416,0.6454973,0.00001521089,0.00005071403,6.274145e-7,0.001359456,0.00150955,0.0005244854,0.03114374,0.07349189,0.2463185],"study_design_scores_gemma":[0.002723169,0.0004858892,0.340808,0.0000614954,0.00003366114,0.00009134025,0.004570941,0.03674734,0.007523899,0.3008931,0.3059569,0.0001043257],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9696872,0.0001167727,0.009725184,0.01146837,0.002080034,0.000835326,0.0000178647,0.00001647126,0.006052811],"genre_scores_gemma":[0.9983561,0.00001891907,0.001010509,0.0003132301,0.00004446426,0.00001465252,3.645535e-7,0.000002371256,0.0002393553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3046893,"threshold_uncertainty_score":0.9971661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.424618148705003,"score_gpt":0.5635768202144353,"score_spread":0.1389586715094323,"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."}}