{"id":"W7053097868","doi":"","title":"In Their Own Words: Learning from NYIFUP Clients about the Value of Representation","year":2022,"lang":"en","type":"other","venue":"Issue Lab (Candid)","topic":"Electromagnetic Compatibility and Measurements","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Representation (politics); Value (mathematics); Quarter (Canadian coin); State (computer science)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003476471,0.0004999886,0.0003715258,0.0005519171,0.02350759,0.01147495,0.001580304,0.005006273,0.01769977],"category_scores_gemma":[0.01200819,0.0003908087,0.0001954375,0.0006602857,0.007614346,0.009200777,0.00483392,0.01119819,0.003087195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004272579,"about_ca_system_score_gemma":0.006347959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0225835,"about_ca_topic_score_gemma":0.04922299,"domain_scores_codex":[0.9966381,0.001747935,0.00005954614,0.0001301679,0.0007080264,0.000716274],"domain_scores_gemma":[0.993897,0.002627492,0.0003605361,0.0002673998,0.001097761,0.001749812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002877143,0.0002411585,0.005128082,0.00009313449,0.000005845711,0.001760176,0.4946228,0.00007724678,0.001044844,0.02137874,0.4084336,0.06718555],"study_design_scores_gemma":[0.000002724403,0.00002373903,0.0009873192,0.0001714341,0.000002826209,0.0008032598,0.7352513,0.00009363627,0.0003217162,0.00196784,0.2603524,0.00002176101],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1679239,0.002987298,0.002842081,0.5304185,0.002135706,0.0001264652,0.0001235897,0.0003334342,0.2931091],"genre_scores_gemma":[0.5321376,0.005668358,0.004100877,0.1168564,0.0006398305,0.0003132958,0.0001024579,0.0005476992,0.3396334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02350759,"threshold_uncertainty_score":0.05921167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156785683426418,"score_gpt":0.2442306518381149,"score_spread":0.2285520834954731,"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."}}