{"id":"W6968703255","doi":"10.5281/zenodo.13973387","title":"Delineating Gender Studies through bibliometric analysis","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Environmental and Biological Research in Conflict Zones","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Field (mathematics); Gender analysis; Qualitative research; Bibliometrics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0132456,0.0005999454,0.001564814,0.135328,0.001740198,0.005631141,0.001358329,0.0006677999,0.008948361],"category_scores_gemma":[0.07536642,0.0002454036,0.0014576,0.1928591,0.00118495,0.004780351,0.003803079,0.0004939427,0.00142628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001570726,"about_ca_system_score_gemma":0.003328739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005639084,"about_ca_topic_score_gemma":0.008044298,"domain_scores_codex":[0.978677,0.006970788,0.003822723,0.001982491,0.007433306,0.001113566],"domain_scores_gemma":[0.9071182,0.0612867,0.01066517,0.005222825,0.01390928,0.001797733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004552829,0.0002268538,0.6284251,0.007430494,0.002488943,0.0005727599,0.01501449,0.0009808369,0.003447362,0.02521108,0.02051791,0.2952289],"study_design_scores_gemma":[0.00005993992,0.0001959605,0.806477,0.001474879,0.001958782,0.0005592765,0.03436082,0.003720342,0.002933301,0.02552622,0.1226251,0.0001083584],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7694729,0.03056896,0.03210319,0.0027408,0.0005056025,0.0009139881,0.06715374,0.0006944885,0.09584633],"genre_scores_gemma":[0.9672104,0.004160217,0.01011019,0.0001173406,0.000276116,0.0007031185,0.01459533,0.000111128,0.002716125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8646719,"threshold_uncertainty_score":0.07005024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1504519216825746,"score_gpt":0.3399632356792198,"score_spread":0.1895113139966453,"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."}}