{"id":"W4382337864","doi":"10.22148/001c.74068","title":"Quantifying the Gap: The Gender Gap in French Writers’ Wikidata","year":2023,"lang":"en","type":"article","venue":"Journal of Cultural Analytics","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gender gap; Representation (politics); Information gap; Diversity (politics); Computer science; Sociology; Political science; Anthropology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008004883,0.0004697051,0.0006768894,0.009110158,0.0037629,0.00644137,0.0007357618,0.0007171677,0.005784819],"category_scores_gemma":[0.04726003,0.0001788985,0.0002717947,0.01128604,0.002679328,0.00564905,0.003537304,0.0008499738,0.00121071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003733946,"about_ca_system_score_gemma":0.002143836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04988752,"about_ca_topic_score_gemma":0.06184743,"domain_scores_codex":[0.991143,0.003447974,0.0006776514,0.001498724,0.002155347,0.001077349],"domain_scores_gemma":[0.9457505,0.03230384,0.008101726,0.003145633,0.009076172,0.001622082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004240568,0.00008337117,0.4394773,0.0007894406,0.0001198539,0.000688007,0.422503,0.000319733,0.001549115,0.02081556,0.01379363,0.09943698],"study_design_scores_gemma":[0.00001864076,0.0001032867,0.5251558,0.0007561996,0.00006135945,0.0005601145,0.3218208,0.001228545,0.001987101,0.008156911,0.1400605,0.00009083799],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.957195,0.002164165,0.003551829,0.002206173,0.0001422068,0.00005935971,0.005702403,0.0001097398,0.02886919],"genre_scores_gemma":[0.9910918,0.000393042,0.001484602,0.0002048154,0.00007267998,0.00009967028,0.002266926,0.00006714073,0.004319396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04988752,"threshold_uncertainty_score":0.09919429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2625698452882957,"score_gpt":0.4320906071308906,"score_spread":0.1695207618425949,"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."}}