{"id":"W2953017024","doi":"10.48550/arxiv.1410.2926","title":"Estimating Open Access Mandate Effectiveness: The MELIBEA Score","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal; University of Ottawa","funders":"","keywords":"Mandate; Predictive power; Predictive value; Actuarial science; Medicine; Statistics; Operations management; Business; Mathematics; Economics; Political science; Internal medicine; Law","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":["metaresearch","bibliometrics","open_science"],"consensus_categories":[],"category_scores_codex":[0.01569876,0.0006480289,0.001523422,0.01819948,0.0007019895,0.003075146,0.001390388,0.0008849642,0.005759596],"category_scores_gemma":[0.07881289,0.0002906803,0.002207602,0.01555585,0.0009050636,0.002323202,0.003042331,0.0007825452,0.001537687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002145288,"about_ca_system_score_gemma":0.00238593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007738207,"about_ca_topic_score_gemma":0.008296744,"domain_scores_codex":[0.9877468,0.003931766,0.001833266,0.001167697,0.004564601,0.0007558766],"domain_scores_gemma":[0.9190525,0.04331069,0.0167546,0.005877687,0.01222486,0.002779566],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003086806,0.0001493355,0.9608765,0.0001613437,0.0005247415,0.00003863904,0.0002400948,0.001714647,0.0002328306,0.001862411,0.002496025,0.03139476],"study_design_scores_gemma":[0.00007100581,0.0002774994,0.9783033,0.00006131717,0.0003025734,0.00007844029,0.0006021847,0.0116219,0.0007987103,0.002181056,0.005648738,0.00005323224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.950748,0.000674745,0.01017976,0.0004481779,0.00004566569,0.000428685,0.01405899,0.000397524,0.02301854],"genre_scores_gemma":[0.9821355,0.000163928,0.008199611,0.00004178005,0.00005633758,0.0005308715,0.00735798,0.00006683837,0.001447254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9986096,"threshold_uncertainty_score":0.08302397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7790319744087093,"score_gpt":0.4990017019218712,"score_spread":0.2800302724868381,"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."}}