{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.007593426,0.0001904181,0.0005068398,0.006824485,0.0006393763,0.002264711,0.0004750711,0.0005409791,0.003589164],"category_scores_gemma":[0.04160934,0.0001108636,0.000426664,0.01155592,0.001112371,0.002549748,0.00189429,0.0004816217,0.0005955719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006881534,"about_ca_system_score_gemma":0.001087415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003217651,"about_ca_topic_score_gemma":0.00293774,"domain_scores_codex":[0.9955893,0.001472225,0.0006012003,0.0006463367,0.001029583,0.0006613185],"domain_scores_gemma":[0.9387748,0.03468667,0.01575992,0.002482211,0.005588902,0.002707452],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000219288,0.00005591772,0.961495,0.0002228174,0.0001500491,0.0001711678,0.01216293,0.000160444,0.0002834298,0.00230051,0.001008716,0.0217697],"study_design_scores_gemma":[0.000008418903,0.00009050078,0.9863769,0.0001011945,0.00005990379,0.0001466572,0.009997291,0.0002232188,0.0002372481,0.0005989428,0.002147391,0.00001227289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993626,0.00108943,0.0001699126,0.0002621,0.00001878824,0.00001027118,0.0006351956,0.000006022597,0.004182224],"genre_scores_gemma":[0.9989821,0.0002315956,0.00009619703,0.00002753482,0.00002571315,0.000009226687,0.0002869277,0.00000396544,0.0003366605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9931755,"threshold_uncertainty_score":0.04015833,"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."}}