Stacking Menageries: Densifying Toronto's Yellowbelt
Bibliographic record
Abstract
What is indifference if not the desire to find oneself among the collective? What builds a community if not the need to connect across islands? The future of collective housing must first forget about collectivity, for interpersonal relationships are established upon the architectural agencies of difference and idiosyncrasy, not those of aggregation, repetition, or consolidation. Houses – individuals with collectivism, and housing – collectives with individualism, have always formed contradicting manifestations of these affects. My thesis, then, seeks an ideal middle ground where architectural individuality is no longer incompatible with stacking - a collection of stacking menageries. This pursuit coincides with Toronto’s search for the same missing middle, in her case, the need for a typology that would densify vast residential areas previously zoned for exclusively detached houses. In a scene of extreme polarization between sprawls of metroburbs and corridors of residential high-rises, my project seeks to incorporate two groups of residents whose needs are excluded by both options: elderlies living with care and young families with children. In this city of contradicting beliefs, I envision a form of housing where repeated living units embrace the peculiarities of every household within, where connections among neighbours and across generations are created by the collection of menageries through the tangible individualities of those that live around, above and below.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".