Perspectives on mixing housing types in the suburbs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Planners typically promote diversity and mixing as desirable social objectives: they assume that mixing housing types will generate social interaction and tolerance. Our research considers what those producing (i.e. planners, developers and municipal leaders) and those consuming (i.e. residents) Canadian suburbs believe mixing housing types can and does achieve. We examined suburban development trends using a comparative case study approach in four provinces. While planners and municipal councillors often advocated the benefits of mixed housing types, developers mitigated the risk of mixing at the neighbourhood scale by employing design strategies that separated particular housing types into price-sensitive pods, and residents remained sceptical about desirability of individual difference at the block level. Planners' ability to encourage block-level mixing in new areas remains constrained by homeowner perspectives that have changed relatively little over decades.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it