{"id":"W3033671873","doi":"10.1016/j.healthplace.2020.102350","title":"Causally speaking: Challenges in measuring gentrification for population health research in the United States and Canada","year":2020,"lang":"en","type":"article","venue":"Health & Place","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Memorial University of Newfoundland; Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Gentrification; Population; Population health; Health equity; Regional science; Sociology; Geography; Health care; Political science; Economic growth; Economic geography; Demography; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.1849453,0.0008295291,0.003195058,0.008430968,0.01819461,0.01478228,0.01046674,0.002462085,0.003171383],"category_scores_gemma":[0.4529861,0.001001317,0.002339284,0.01482658,0.01624603,0.006810853,0.01239185,0.007719834,0.0002830218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05349215,"about_ca_system_score_gemma":0.1910197,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9608427,"about_ca_topic_score_gemma":0.9755593,"domain_scores_codex":[0.8611611,0.08523473,0.01003409,0.01002578,0.02598611,0.007558248],"domain_scores_gemma":[0.7199043,0.1599438,0.01893465,0.02562403,0.06747992,0.008113235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001783252,0.0001833782,0.4258687,0.002060604,0.002578714,0.0001757297,0.02463621,0.004972614,0.0002610098,0.3270296,0.06619671,0.1458585],"study_design_scores_gemma":[0.0001442271,0.0001198443,0.3966157,0.009678808,0.001582014,0.0002436547,0.06148387,0.01495675,0.001885879,0.3762846,0.1365974,0.0004073677],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2867561,0.03964358,0.190568,0.31931,0.007297254,0.003449042,0.02057529,0.0005611273,0.1318396],"genre_scores_gemma":[0.9182019,0.004218359,0.05169834,0.01878828,0.0005014849,0.001179763,0.002676974,0.0002323022,0.002502527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1849453,"threshold_uncertainty_score":0.9780961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4230306409572265,"score_gpt":0.4549733235613355,"score_spread":0.03194268260410893,"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."}}