{"id":"W6967867099","doi":"10.5281/zenodo.14163843","title":"Mapping Open Science Scholarly Literature","year":2024,"lang":"en","type":"article","venue":"ZBW Publication Archive (ZBW – Leibniz Information Centre for Economics)","topic":"Academic Publishing and Open Access","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Dalhousie University","funders":"Deutsche Forschungsgemeinschaft","keywords":"Open science; Field (mathematics); Open data; Scholarly communication; Citizen journalism; Citizen science; Open research; Knowledge production; Information science","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","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0104717,0.0002766617,0.000340388,0.002314432,0.001505941,0.1307938,0.01122749,0.0001788257,0.0004363494],"category_scores_gemma":[0.0138349,0.0002366632,0.0001921791,0.002180876,0.0003225814,0.1332856,0.002456249,0.0006284749,0.001789542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003832704,"about_ca_system_score_gemma":0.001233476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000704965,"about_ca_topic_score_gemma":0.00003429985,"domain_scores_codex":[0.9960028,0.00009819783,0.001637987,0.0008836548,0.0007436142,0.0006337378],"domain_scores_gemma":[0.9943751,0.0009949761,0.0009616184,0.001417431,0.00181211,0.0004387895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003052868,0.00001939161,0.002133647,0.00003688657,0.00002750973,2.41107e-7,0.007146601,0.0001889769,0.00002807619,0.4566267,0.2748213,0.2589401],"study_design_scores_gemma":[0.0004114778,0.0000132849,0.006273276,0.0000821342,0.000004744688,0.00001032288,0.001425614,0.08597785,0.000116717,0.05608291,0.8493237,0.0002779603],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06711602,0.0004569135,0.1558897,0.2553694,0.008364676,0.006498493,0.006406574,0.0007935369,0.4991046],"genre_scores_gemma":[0.9267808,0.0003224892,0.02933049,0.014997,0.001325687,0.0005500212,0.005534886,0.00008771644,0.02107098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8596647,"threshold_uncertainty_score":0.9997939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05276225711009284,"score_gpt":0.3532230362133776,"score_spread":0.3004607791032847,"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."}}