{"id":"W7014243416","doi":"","title":"Planning context and urban intensification outcomes: Sydney versus Toronto","year":2011,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Urban Planning and Governance","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"NIMBY; Metropolitan area; Framing (construction); Incentive; Politics; Context (archaeology); Government (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"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":[0.0005511443,0.0001805253,0.000195214,0.0007985111,0.002977883,0.002540675,0.0004363121,0.0002630978,0.004534431],"category_scores_gemma":[0.002427837,0.0001438376,0.0001221132,0.002093698,0.002869315,0.000781677,0.002824174,0.0006904589,0.0001559278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02501716,"about_ca_system_score_gemma":0.0109063,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8266394,"about_ca_topic_score_gemma":0.9540098,"domain_scores_codex":[0.9992673,0.000197133,0.00002348649,0.00004907645,0.0001642771,0.0002987577],"domain_scores_gemma":[0.9986842,0.0001870116,0.0003651732,0.00004563805,0.0002569329,0.0004610396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003683737,0.0002135961,0.6367587,0.0003168989,0.00007806232,0.002291229,0.2727475,0.003387148,0.001389957,0.04427856,0.008278613,0.02989142],"study_design_scores_gemma":[0.0000147821,0.00009214651,0.7952297,0.00012911,0.00002716222,0.0001318904,0.1852723,0.0005061357,0.0003660736,0.0009877044,0.01721125,0.00003164946],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680601,0.000198406,0.00007202413,0.0008595721,0.000007093323,0.00002363706,0.0002119962,0.000004967845,0.03056218],"genre_scores_gemma":[0.9977951,0.0001667512,0.00004584263,0.00002893383,0.000002720476,0.000009236718,0.00007883961,0.000002734569,0.001869895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1733606,"threshold_uncertainty_score":0.348763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1012600776976602,"score_gpt":0.3563355071736788,"score_spread":0.2550754294760186,"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."}}