{"id":"W4386031958","doi":"10.21203/rs.3.rs-3200991/v1","title":"Community Resilience and Migration: Using Best Evidence Synthesis to Promote Migrant Welfare","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Climate Change, Adaptation, Migration","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Empowerment; Psychological resilience; Stressor; Resilience (materials science); Settlement (finance); Community resilience; Welfare; Economic growth; Political science; Socioeconomics; Sociology; Business; Psychology; Social psychology; Economics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.01091335,0.0002717831,0.0003681093,0.0007118724,0.004477338,0.000909148,0.001022786,0.0004034003,0.0001186851],"category_scores_gemma":[0.02262759,0.0002905438,0.00009932106,0.001617693,0.0006946765,0.0004583491,0.001408795,0.001475747,0.0001232324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126248,"about_ca_system_score_gemma":0.0008958975,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2380223,"about_ca_topic_score_gemma":0.4858462,"domain_scores_codex":[0.9913883,0.004286177,0.0004511422,0.0007319946,0.002306224,0.0008362237],"domain_scores_gemma":[0.9940135,0.002743279,0.000165723,0.0009611024,0.001688506,0.0004278295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003264375,0.0006392354,0.04952136,0.007162017,0.0001265423,0.0001162921,0.8832692,0.003597763,0.007068109,0.003160919,0.007183474,0.03782867],"study_design_scores_gemma":[0.000297662,0.0008345372,0.1652066,0.04013066,0.0002459245,0.00001058199,0.7076153,0.01801908,0.002054306,0.007442603,0.05570327,0.002439439],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9596881,0.000879408,0.000271103,0.03551597,0.0002553814,0.00268909,0.0002039011,0.0002074284,0.000289604],"genre_scores_gemma":[0.9933521,0.00269038,0.001812916,0.00002890194,0.0003686877,0.000564268,0.00004512886,0.00005474329,0.001082939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2478239,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5068962824057445,"score_gpt":0.4978217372820802,"score_spread":0.009074545123664235,"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."}}