{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"8fbe39a5f0e6","filters":{"venue":"The Journal of Social Science"}},"results":[{"id":"W3199541760","doi":"10.30520/tjsosci.920636","title":"THE DIVERSITY OF GERMAN TOP ORGANISATIONS’ WEBSITES AND SOCIAL MEDIA","year":2021,"lang":"en","type":"article","venue":"The Journal of Social Science","topic":"Gender Diversity and Inequality","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Safe Drinking Water Foundation","funders":"","keywords":"German; Diversity (politics); Social media; Ranking (information retrieval); Public relations; Business; Political science; Computer science; Linguistics","authors":[{"name":"Dominik Pietzcker","is_ca":true},{"name":"Lara LÜTHGENS","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08437867173101919,"gpt":0.314176430086793,"spread":0.2297977583557738,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001694486,0.0002449086,0.0001640401,0.004468138,0.001965156,0.004645771,0.0002416972,0.000383491,0.004610686],"category_scores_gemma":[0.004735517,0.0001388686,0.000140775,0.003833893,0.001603954,0.00267077,0.002623556,0.0003648522,0.0007706548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224025,"about_ca_system_score_gemma":0.0006092379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004368071,"about_ca_topic_score_gemma":0.005376705,"domain_scores_codex":[0.9969553,0.001193161,0.0001634301,0.0003091421,0.0008718735,0.0005071945],"domain_scores_gemma":[0.9947494,0.002745578,0.001022084,0.0003447246,0.0006186793,0.0005196343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004528636,0.0001621183,0.4285749,0.001235875,0.0002031206,0.002670137,0.2459806,0.0007472735,0.008733472,0.04060893,0.01433591,0.2562948],"study_design_scores_gemma":[0.0000128281,0.000124777,0.6277157,0.0006614793,0.00009150687,0.001663952,0.2471885,0.001185493,0.004008971,0.004353822,0.1128882,0.000104729],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959577,0.0008092046,0.00081602,0.0008712405,0.00003348675,0.00001776949,0.0006694436,0.00003657423,0.03716927],"genre_scores_gemma":[0.997654,0.0001982277,0.0001783225,0.00005736167,0.00001476879,0.000008021875,0.0002273866,0.00001204413,0.001649942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004645771,"threshold_uncertainty_score":0.01542431,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386740261","doi":"10.30520/tjsosci.1332143","title":"PANEL DATA ANALYSIS MAKING EFFECT THE VARIABLES ON INCOME DISTRIBUTION INJUSTICE","year":2023,"lang":"en","type":"article","venue":"The Journal of Social Science","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Gini coefficient; Economics; Injustice; Income distribution; Per capita income; Distribution (mathematics); Investment (military); Demographic economics; Panel data; Economic inequality; Welfare; Inequality; Econometrics; Sociology; Demography; Political science; Mathematics","authors":[{"name":"Yeşim KUBAR","is_ca":false},{"name":"Yasemin Cicek Schoenberg","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08469443058726064,"gpt":0.3974071951250187,"spread":0.3127127645377581,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002705379,0.0004461788,0.0005136883,0.001262761,0.0006031811,0.0009675053,0.0005266467,0.0004714788,0.01343479],"category_scores_gemma":[0.009530422,0.0001730692,0.001251007,0.002592994,0.0002417451,0.0005072409,0.000718015,0.00148834,0.00130449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004900348,"about_ca_system_score_gemma":0.0009432214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01990416,"about_ca_topic_score_gemma":0.01256031,"domain_scores_codex":[0.997216,0.001538961,0.0001697368,0.0004301963,0.0003415769,0.0003035398],"domain_scores_gemma":[0.9891002,0.006423478,0.001823518,0.0009314549,0.001353279,0.0003681694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000295256,0.0002814455,0.9395329,0.0001536664,0.001394464,0.0003774879,0.0006649044,0.007474648,0.0005598295,0.004236529,0.01652526,0.02850359],"study_design_scores_gemma":[0.0000416826,0.000490583,0.9290636,0.0001411256,0.00105554,0.0001706026,0.002680866,0.03902818,0.001621605,0.003666908,0.02197837,0.00006088926],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9041518,0.000971052,0.03140668,0.001898912,0.0003083033,0.0002782283,0.0455211,0.0002468575,0.01521718],"genre_scores_gemma":[0.9737844,0.0003214271,0.003923376,0.000155087,0.00006785958,0.000264312,0.01673986,0.00002066159,0.004723048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01990416,"threshold_uncertainty_score":0.04494387,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}