{"id":"W4413297425","doi":"10.1177/20539517251368242","title":"Gender data for good? Partnerships between tech companies and humanitarian and development organizations","year":2025,"lang":"en","type":"article","venue":"Big Data & Society","topic":"International Development and Aid","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"High tech; Public relations; Business; Political science; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007418044,0.00008260368,0.0001035061,0.00002294376,0.001109547,0.0001904608,0.0006759458,0.00006854732,0.00001821087],"category_scores_gemma":[0.0002011196,0.00008214034,0.000008003716,0.0001818376,0.0001662091,0.0003275066,0.0008811222,0.00005205596,0.000004147718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004884884,"about_ca_system_score_gemma":0.0005279786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001106446,"about_ca_topic_score_gemma":0.001951264,"domain_scores_codex":[0.9991486,0.0000287825,0.0001582871,0.0003338302,0.0001554796,0.0001750371],"domain_scores_gemma":[0.9993145,0.0001719919,0.00004260516,0.0003074615,0.0001170571,0.00004640725],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006221079,0.00008606433,0.1734818,0.0001544246,0.0006674754,5.330616e-7,0.04225951,2.146289e-7,0.00005339323,0.378197,0.3811627,0.02393072],"study_design_scores_gemma":[0.0002322323,0.000002656552,0.06150879,0.00001953124,0.00003441117,1.140743e-7,0.008208194,0.00005089207,0.00002184813,0.002433495,0.9273546,0.0001331885],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4202195,0.008960176,0.3050613,0.1245261,0.006595668,0.009226637,0.02307425,0.001760565,0.1005759],"genre_scores_gemma":[0.9605625,0.0003148425,0.02692554,0.0005855781,0.0004141369,0.00001424675,0.009058356,0.00001162767,0.002113147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5461919,"threshold_uncertainty_score":0.8533852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3761430581028499,"score_gpt":0.3843339392009337,"score_spread":0.008190881098083747,"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."}}