'The public' and planning in Toronto
Bibliographic record
Abstract
As both a science and an art, planning is regulated by public policy, concerned with shaping and guiding the physical growth and arrangement of natural and built environments. Within these environments what is considered 'public' and how 'the public' is used in planning discourses and planning tools is based on constantly changing, socio-political contexts. Shaped by planning decisions, 'the public' often learns about transformations of urban space through stories in the media. These planning stories help 'the public' understand and relate to their physical environments by ascribing meaning to space. Through a case study of the Mirvish + Gehry development, this research substantiates the importance of telling a good story about 'the public' and 'the public good' in relation to development. Mirvish + Gehry invested heavily in telling their version of a planning story. By funding and staging numerous appearances, centred on the benefits of his development, Mirvish embodied his story and his development's brand by exercising his social leverage, capital, power and privilege, all of which afforded him the attention of the media and therefore 'the public'. In a Toronto context, place-based psychological ties to the community - like the Mirvish family history - are often found in discourses of legacy, art and the future. These ties have become useful tools for private and public development to build emotional connection to spatial environments. Both theoretically and in practice, planning stories help to build support and 'common-sense' application of future, public spaces, by leveraging current placebased attachments through neoliberal narratives in the media. Developers and politicians have realized the potential of partnering with arts communities, through growth coalitions, to tell more persuasive and poignant stories. As MP Adam Vaughan stated (Galati 2015c), it is the 'coolness factor' that arts and culture bring to development that helps it stand out in a fiercely competitive global market.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".