{"id":"W4239970099","doi":"10.3138/jsp.44.1.61","title":"Editing Academic Books in the Humanities and Social Sciences: Maximizing Impact for Effort","year":2012,"lang":"en","type":"article","venue":"Journal of Scholarly Publishing","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scholarship; Argument (complex analysis); Promotion (chess); Quality (philosophy); Hegemony; Face (sociological concept); Sociology; Digital humanities; Public relations; Political science; Library science; Social science; Computer science; Epistemology; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","sts","scholarly_communication"],"consensus_categories":["metaresearch","bibliometrics","scholarly_communication"],"category_scores_codex":[0.1947378,0.000124275,0.0003124608,0.03027161,0.001379488,0.1270533,0.003617535,0.0001883447,0.00002451642],"category_scores_gemma":[0.0945478,0.00006624651,0.0002046037,0.0296052,0.0003009613,0.1017879,0.0004571699,0.002191873,0.000002032202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190347,"about_ca_system_score_gemma":0.0003203682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004955195,"about_ca_topic_score_gemma":0.000005375439,"domain_scores_codex":[0.9889376,0.0003226789,0.001094318,0.0002307876,0.008581516,0.0008331095],"domain_scores_gemma":[0.9880593,0.007219453,0.001118605,0.0001422409,0.003308875,0.0001514925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003067013,0.00004899559,0.8901775,0.00001008386,0.00001692934,0.000004925674,0.00705127,0.00001144406,0.0005374215,0.00656549,0.04030842,0.05523679],"study_design_scores_gemma":[0.001142149,0.0003719283,0.8520878,0.00007619581,0.0000187212,0.0002705747,0.03095988,0.0005144723,0.0001068299,0.02353746,0.09069429,0.00021971],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841406,0.004373451,0.001215835,0.006194114,0.0006750426,0.0001687807,0.00001054008,0.00000459368,0.003217002],"genre_scores_gemma":[0.9950859,0.00005778077,0.001654229,0.001258796,0.001856829,0.000003473831,4.34924e-7,0.000007798183,0.00007474462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.10019,"threshold_uncertainty_score":0.9999206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7397174462705441,"score_gpt":0.5726202859908598,"score_spread":0.1670971602796844,"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."}}