{"id":"W4210308673","doi":"10.32920/19027607.v1","title":"Using tax increment financing to develop affordable housing in Toronto","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Affordable housing; Finance; Plan (archaeology); Business; Multitude; State (computer science); Economic growth; Political science; Economics; Geography; Computer science","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":[0.000461859,0.0001131706,0.00006276249,0.0005784965,0.001452262,0.001805828,0.000336772,0.0003288089,0.003601664],"category_scores_gemma":[0.001940929,0.000142408,0.0001357463,0.00108181,0.000863387,0.0004853427,0.000823934,0.0005159914,0.0001197901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04975626,"about_ca_system_score_gemma":0.0317998,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9409395,"about_ca_topic_score_gemma":0.9735554,"domain_scores_codex":[0.9994096,0.00009711507,0.00001742806,0.00003057348,0.0001434614,0.0003019086],"domain_scores_gemma":[0.9992697,0.0001509461,0.0001138027,0.00002992995,0.0001862785,0.0002493819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000208781,0.0001373078,0.2420986,0.0004603557,0.00008261666,0.002168406,0.0109872,0.03092299,0.001973277,0.5544035,0.0877564,0.06880062],"study_design_scores_gemma":[0.0001179765,0.0001561726,0.6221337,0.0003772875,0.0001309706,0.0002752905,0.02047058,0.03320788,0.002798081,0.0143437,0.3059005,0.00008780362],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8418899,0.002094165,0.001545776,0.01337068,0.00006564803,0.0001725582,0.002331994,0.00005140159,0.1384778],"genre_scores_gemma":[0.9874293,0.0009991706,0.0006499969,0.0001598198,0.00001114528,0.00002303345,0.0003466224,0.000003905272,0.01037691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05906051,"threshold_uncertainty_score":0.3610086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07154912608898649,"score_gpt":0.2683115131811724,"score_spread":0.1967623870921859,"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."}}