{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001160423,0.00009012236,0.0001650462,0.00005308761,0.0002797499,0.00005819059,0.0002397513,0.0001094813,0.00007619055],"category_scores_gemma":[0.001107845,0.00009371511,0.00003186927,0.00005453471,0.0003537778,0.0002623752,0.0000612678,0.0002261866,0.000009436029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005334089,"about_ca_system_score_gemma":0.0001485403,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008089975,"about_ca_topic_score_gemma":0.0134428,"domain_scores_codex":[0.9987641,0.0001476865,0.0002012357,0.0003016389,0.0001605823,0.0004247809],"domain_scores_gemma":[0.9990147,0.0004644754,0.00007361174,0.0002295677,0.00006265181,0.0001549479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004234622,0.00009264311,0.7842817,0.00001373216,0.00006442959,0.00002420601,0.09550938,0.000005204935,0.0000267503,0.02498063,0.002533066,0.09204477],"study_design_scores_gemma":[0.001625718,0.0002531241,0.4653088,0.0001197852,0.00000889338,0.000001910402,0.1270745,0.0004058071,0.00004676104,0.0003863599,0.4043321,0.0004362118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6092719,0.0003644873,0.000001226609,0.0002605939,0.0003596009,0.0001834659,0.00001107007,0.00003723465,0.3895104],"genre_scores_gemma":[0.9944236,0.001057503,0.0001291065,0.00009169652,0.00008224041,0.00002207568,0.000003941638,0.00001202145,0.004177867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.401799,"threshold_uncertainty_score":0.9985152,"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."}}