{"id":"W2604895157","doi":"10.7710/2162-3309.2104","title":"Open Access Policies and Academic Freedom: Understanding and Addressing Conflicts","year":2017,"lang":"en","type":"article","venue":"Journal of Librarianship and Scholarly Communication","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Academic freedom; Context (archaeology); Political science; Public relations; Commercialization; Vagueness; Public administration; Freedom of information; Higher education; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.02438986,0.0001059527,0.0003193122,0.008434123,0.001979369,0.1390699,0.009499614,0.0001629482,0.00002324954],"category_scores_gemma":[0.02640725,0.00007483061,0.00003549383,0.005038121,0.0005624656,0.04702019,0.007856622,0.001062434,0.000001238291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004492909,"about_ca_system_score_gemma":0.0001817821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001614925,"about_ca_topic_score_gemma":0.00001831126,"domain_scores_codex":[0.99635,0.0005214204,0.0006810686,0.0002295054,0.001995065,0.0002228928],"domain_scores_gemma":[0.9932629,0.003306595,0.001281511,0.00091692,0.0008094623,0.0004226141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006230659,0.0000214788,0.9136091,0.000007102587,0.00002596715,0.000004608967,0.001711387,0.000001209225,0.0007617217,0.03300374,0.002362286,0.04842913],"study_design_scores_gemma":[0.0008905741,0.00008139681,0.8724763,0.0001225473,0.00001280486,0.00006234083,0.001873742,0.0003966514,0.0001090169,0.1183156,0.005554319,0.0001046821],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9499938,0.01208463,0.001920807,0.02592671,0.0001614486,0.0001677586,0.000007696194,0.000004760817,0.00973241],"genre_scores_gemma":[0.9877113,0.009942224,0.001789176,0.0002609849,0.00006587068,0.000001049996,6.14867e-7,0.000007470123,0.0002212952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09204967,"threshold_uncertainty_score":0.9993199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9468948604005198,"score_gpt":0.6709462950792823,"score_spread":0.2759485653212376,"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."}}