{"id":"W4401023374","doi":"10.31274/jlsc.17755","title":"Staffing of Library Publishing Programs in the United States and Canada: A Data-Driven Analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Librarianship and Scholarly Communication","topic":"Intellectual Property Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003302058,0.0006158937,0.000868395,0.009164282,0.002664472,0.003378035,0.002510109,0.0005982567,0.003483479],"category_scores_gemma":[0.01531305,0.0006602348,0.001885036,0.0236539,0.001161143,0.0009713313,0.00224993,0.001430172,0.0009765774],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04867683,"about_ca_system_score_gemma":0.1048784,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9939626,"about_ca_topic_score_gemma":0.9930068,"domain_scores_codex":[0.9951228,0.0003258814,0.0003286247,0.0006200148,0.002458978,0.00114373],"domain_scores_gemma":[0.980642,0.001970758,0.001942083,0.0006200132,0.01306104,0.001764067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001223884,0.00008581636,0.9347049,0.000312816,0.0002873484,0.0001048471,0.001199655,0.003537553,0.0001099873,0.001483833,0.04518608,0.01286494],"study_design_scores_gemma":[0.00003000568,0.0000411254,0.9514134,0.000310412,0.0001260618,0.00006671772,0.003994588,0.006508495,0.0004762264,0.0004357744,0.03650209,0.00009514277],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4136018,0.000988532,0.00165033,0.002262937,0.00004496124,0.0004036822,0.5738119,0.0003178082,0.006918208],"genre_scores_gemma":[0.6634102,0.001271187,0.007363383,0.0008087868,0.00005006522,0.0009676169,0.3198118,0.0001801139,0.006136898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.996622,"threshold_uncertainty_score":0.3531768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1323533189370881,"score_gpt":0.2968359794862067,"score_spread":0.1644826605491185,"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."}}