{"id":"W3208386177","doi":"10.1145/3462204.3481798","title":"Coordinating Migration: Caring for Communities & Their Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Public relations; Context (archaeology); Big data; Government (linguistics); Service provider; Analytics; Immigration; Knowledge management; Joins; Sociology; Service (business); Business; Internet privacy; Data science; Political science; Computer science; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04035896,0.0006065724,0.0007015808,0.003402103,0.03777675,0.02108166,0.003494758,0.004688571,0.006661698],"category_scores_gemma":[0.09080806,0.0008332771,0.0006789928,0.003888909,0.02349139,0.0208788,0.03229948,0.007368514,0.001282183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008349617,"about_ca_system_score_gemma":0.05706529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07459243,"about_ca_topic_score_gemma":0.1098208,"domain_scores_codex":[0.9569612,0.03139153,0.001555712,0.002544027,0.003841853,0.003705733],"domain_scores_gemma":[0.9335731,0.02491144,0.004451121,0.00748584,0.01263883,0.01693969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005176284,0.00008327272,0.01967767,0.0003472239,0.00004865266,0.001375194,0.7823318,0.0001827013,0.0007898602,0.0363212,0.04972904,0.1090617],"study_design_scores_gemma":[0.000008040624,0.00003621331,0.002412577,0.0006219903,0.00001837106,0.0004918389,0.7875783,0.0003010895,0.0002376922,0.0266473,0.1815927,0.00005379013],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3216076,0.007764184,0.06546585,0.4796362,0.003561118,0.001001077,0.0004986302,0.0007439714,0.1197214],"genre_scores_gemma":[0.9117044,0.004587831,0.04466005,0.02168986,0.000475489,0.0004980313,0.0003640975,0.0002648444,0.01575529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07459243,"threshold_uncertainty_score":0.2134411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1975748796103419,"score_gpt":0.3863856607371162,"score_spread":0.1888107811267743,"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."}}