{"id":"W6967247790","doi":"10.5281/zenodo.10079302","title":"McGill Data Anonymization Workshop Series - 3. Ethically sharing qualitative data","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Presentation (obstetrics); Data sharing; Data access; Data anonymization; Data presentation; Qualitative research; Ethical issues; Qualitative property","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005787587,0.0001049651,0.0001431351,0.0002282517,0.006050779,0.001261045,0.004745127,0.00006370499,0.005183241],"category_scores_gemma":[0.008018948,0.0001123477,0.00002420789,0.001670529,0.0003335035,0.001858904,0.009191996,0.0002850302,0.004583385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006132581,"about_ca_system_score_gemma":0.000009341175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001775161,"about_ca_topic_score_gemma":0.00006620253,"domain_scores_codex":[0.996893,0.001049516,0.0002726924,0.0007325582,0.000636723,0.0004155417],"domain_scores_gemma":[0.9976855,0.0001662249,0.0001177555,0.001543505,0.0003159836,0.0001710276],"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":[0.00004616416,0.00009952736,0.00001249498,0.000061019,0.000137009,0.00002310012,0.04532398,0.0001568653,0.0006898099,0.1182867,0.6635852,0.1715781],"study_design_scores_gemma":[0.0001067006,0.00001873117,0.0001431332,0.00003468918,0.00001868682,0.000002395761,0.01496475,0.0038822,0.000006345833,0.00123097,0.9794474,0.0001439664],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02300245,0.0002067768,0.03990102,0.06468414,0.0008195328,0.002072197,0.02915824,0.01030573,0.8298499],"genre_scores_gemma":[0.7261292,0.003015356,0.004458876,0.0008812097,0.001227982,1.436842e-7,0.2382788,0.002669763,0.02333863],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8065113,"threshold_uncertainty_score":0.9997758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3036681594580979,"score_gpt":0.421557183239662,"score_spread":0.117889023781564,"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."}}