{"id":"W2277027064","doi":"","title":"Trading Density for Benefits: Toronto and Vancouver Compared","year":2013,"lang":"en","type":"preprint","venue":"TSpace (University of Toronto)","topic":"Housing, Finance, and Neoliberalism","field":"Economics, Econometrics and Finance","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; TD Bank","keywords":"Negotiation; Transparency (behavior); Amenity; Cash; Business; Finance; Political science; Law","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005086256,0.000272172,0.000550306,0.002792757,0.002504473,0.004901964,0.0009057839,0.0005496088,0.02364215],"category_scores_gemma":[0.005202664,0.0002122163,0.0002970607,0.008885197,0.0009345159,0.001159878,0.001539966,0.001110859,0.001523351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02081903,"about_ca_system_score_gemma":0.01326666,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9811237,"about_ca_topic_score_gemma":0.9940269,"domain_scores_codex":[0.9990798,0.00007877575,0.00004299636,0.0001128088,0.0004406179,0.0002448762],"domain_scores_gemma":[0.996215,0.0004278615,0.0003749365,0.0002084858,0.001933892,0.0008398276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006784709,0.0001105579,0.6755154,0.0002312575,0.0002640127,0.0009649653,0.01255656,0.003236114,0.000335588,0.05224327,0.1752504,0.07861354],"study_design_scores_gemma":[0.00002884759,0.00002983725,0.8970419,0.0001566387,0.00009623192,0.0001473461,0.01825399,0.001790141,0.0002035488,0.002614049,0.07958381,0.0000536312],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7832342,0.007162258,0.0005348407,0.006578041,0.0002870188,0.00005815962,0.03503022,0.00009043712,0.1670248],"genre_scores_gemma":[0.9432872,0.001408607,0.0001511419,0.0001613488,0.00003178496,0.00001338297,0.009401043,0.00006112488,0.04548433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02364215,"threshold_uncertainty_score":0.1510533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03512910127635249,"score_gpt":0.2161857728549816,"score_spread":0.1810566715786291,"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."}}