{"id":"W4243951141","doi":"10.31235/osf.io/px8kt","title":"The Diverse Niches of Megajournals: Specialism within Generalism","year":2019,"lang":"en","type":"preprint","venue":"","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Ecological niche; Publishing; Diversity (politics); Generalist and specialist species; Discipline; Niche; Novelty; Popularity; Originality; Scope (computer science); Competition (biology); Sociology; Biology; Ecology; Social science; Political science; Computer science; Psychology; Anthropology","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"],"consensus_categories":[],"category_scores_codex":[0.004521762,0.0001939763,0.0005357302,0.007795249,0.002119513,0.009861917,0.0008123728,0.0008281569,0.009252902],"category_scores_gemma":[0.01690361,0.0002506523,0.0004044046,0.01115906,0.004106831,0.007039213,0.006571779,0.0009435914,0.001173278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002557044,"about_ca_system_score_gemma":0.001830277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280737,"about_ca_topic_score_gemma":0.002798144,"domain_scores_codex":[0.9959018,0.001199855,0.0003133948,0.0007319305,0.001076548,0.000776416],"domain_scores_gemma":[0.9619753,0.01170643,0.008525595,0.003958049,0.003548158,0.01028644],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001311406,0.0002479822,0.4795051,0.001373222,0.0002844301,0.001923563,0.07675596,0.00071995,0.008112258,0.1809901,0.01425584,0.2345201],"study_design_scores_gemma":[0.00004573401,0.0002731475,0.7626128,0.0005016591,0.00008034388,0.001854908,0.0350717,0.000729607,0.001585165,0.04287345,0.1542973,0.00007426287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9377656,0.00570048,0.003113201,0.003955817,0.0001218597,0.0000436999,0.0007913841,0.0001975949,0.04831035],"genre_scores_gemma":[0.9944432,0.0007965197,0.0009705794,0.0003101726,0.0001451659,0.00001316829,0.0003570996,0.00006690142,0.002897022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9954782,"threshold_uncertainty_score":0.03095406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7794564199695639,"score_gpt":0.6084614387705788,"score_spread":0.1709949811989852,"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."}}