New urbanist housing in Toronto, Canada: a critical examination of the structures of \nprovision and housing producer practices
Bibliographic record
Abstract
The empirical focus for this thesis research is Toronto, Canada where four \ncase study sites are investigated and fifty-seven semi-structured interviews \nconducted with a range of actors both directly and indirectly involved in the \ncreation of New Urbanist-inspired development projects. Two of the sample \nprojects are situated in greenfield locations outside the administrative \nboundary of the City of Toronto, and two are situated in brownfield locations \non formerly developed lands, both within the urban core of the City of \nToronto. The contrasting contexts of the study units have been purposefully \nselected to explore the possibility of multi-factor causality involving contrasts \nof place, process, time, and social interaction. \nUnderpinning this empirical research is the contention that the structures of \nprovision model provides a useful approach for framing housing production \nresearch. However, it is argued that the evaluative power of this approach is \nlimited by its inability to adequately account for how and why the New \nUrbanist form of provision has emerged, been legitimised, and normalised as \n'best practice' within Toronto. In an unorthodox move, the final chapter of this \nthesis takes the level of theorisation enabled via the empirical framework of \nthe structures of provision a step further to address this shortcoming. This is \ndone by applying a 'rationalities' perspective to the investigation of how and \nwhy New Urbanism has become such a powerful force within Toronto's \ndevelopment cultures.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".