{"id":"W2605441625","doi":"","title":"Do Efforts to Mitigate Gentrification Work? Evidence from Washington DC","year":2017,"lang":"en","type":"article","venue":"Alternate routes","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Gentrification; Work (physics); Political science; Economics; Engineering; Economic growth; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005325439,0.0001428522,0.0001921592,0.00005358735,0.001671231,0.0009298319,0.0008047352,0.00006923149,0.0001460505],"category_scores_gemma":[0.001198304,0.0001310851,0.00007603768,0.0001031445,0.0001735838,0.0008631809,0.0001392594,0.00009608541,0.0005007962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000886959,"about_ca_system_score_gemma":0.0000508023,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01835334,"about_ca_topic_score_gemma":0.002739307,"domain_scores_codex":[0.9984269,0.00009710691,0.0002363151,0.0003980199,0.0004903015,0.0003513536],"domain_scores_gemma":[0.9984888,0.0003500783,0.0002463431,0.0005674353,0.0001620487,0.0001852253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004513039,0.00003718068,0.8988465,0.000006490594,0.0001034888,0.00001458525,0.04127575,0.00003292726,0.0007736753,0.005116378,0.006377602,0.04737025],"study_design_scores_gemma":[0.0005826323,0.00006410767,0.8305878,0.0007130512,0.0001111524,5.008498e-7,0.002450015,0.0001258165,0.008596119,0.01184162,0.1441557,0.0007714799],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770864,0.00130231,0.0005026365,0.009529964,0.001271392,0.0003151579,0.00001403569,0.0001105646,0.009867548],"genre_scores_gemma":[0.992815,0.001191143,0.0004534484,0.0002454652,0.000842342,0.00003682167,0.0000049933,0.00001354062,0.004397282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1377781,"threshold_uncertainty_score":0.9996285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07473838240047094,"score_gpt":0.3452801662210165,"score_spread":0.2705417838205456,"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."}}