{"id":"W4404565753","doi":"10.1371/journal.pone.0312731","title":"Gender differences in representation, citations, and h-index: An empirical examination of the field of communication across the ten most productive countries","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advertising and Communication Studies","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gender gap; Gender disparity; Productivity; Social Sciences Citation Index; China; Index (typography); Representation (politics); Gender bias; Demographic economics; Citation; Citation index; Political science; Demography; Geography; Social science; Sociology; Science Citation Index; Economic growth; Psychology; Social psychology; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006377806,0.00003723744,0.00008805181,0.00002656846,0.0003959858,0.00004178309,0.0002293194,0.00003153336,0.000004941071],"category_scores_gemma":[0.000855991,0.00002370312,0.00001119965,0.0003873517,0.0005363089,0.000183844,0.00008620597,0.00009531072,3.49529e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001945615,"about_ca_system_score_gemma":0.00005350606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000935142,"about_ca_topic_score_gemma":0.002453076,"domain_scores_codex":[0.9989001,0.0005101269,0.0001666019,0.00009208169,0.0002695567,0.00006155115],"domain_scores_gemma":[0.998356,0.001059497,0.00007594951,0.0002818547,0.0002176569,0.000008975617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009669746,0.0002302777,0.6237031,0.00004412526,0.0000720348,4.173679e-8,0.3615136,0.000004315524,0.0001046247,0.01058413,0.0001010269,0.003633073],"study_design_scores_gemma":[0.00005096537,0.00001564871,0.9265259,0.0001119301,0.00002269285,6.205341e-8,0.06718306,0.0003589237,0.0007177751,0.004912551,0.00006668201,0.00003379981],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838097,0.00158381,0.00006203448,0.01301646,0.00001617717,0.0002067404,0.000005922109,0.00001446566,0.001284697],"genre_scores_gemma":[0.9984989,0.001059972,0.0001241772,0.00007360704,0.00001565616,0.00003331475,0.000002417103,0.000002441442,0.0001894951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3028228,"threshold_uncertainty_score":0.3045643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1377517547657002,"score_gpt":0.4054980937324401,"score_spread":0.2677463389667398,"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."}}