Bibliographic record
Abstract
This thesis presents a portrait of cultural diversity filtered through a lens of memory, experience and architecture. What does diversity look like? Where do we experience diversity? How unrestrained is our experience of it? Although cultural identity is tied to both personal experience and memory, Toronto’s experience of diversity has evolved with the growth of the city. Consequently, Toronto’s cultural diversity is today experienced through a limited and problematic architectural and marketing-based framework. These frameworks make ethnicity more accessible, but also limit our experience of it. I propose to release these limitations by highlighting the frameworks within which we view our various ethnicities. These are 1) the marketing of ethnic products to consumers (specifically Loblaws Presidents Choice, No Name and Memories Of…products) and 2) architectural uniformity. I examine these issues by recounting personal experiences with my family in South Western Ontario; by conducting a typological study of Toronto’s storefront restaurants – a portrait of a city which expands on the representation of industrial landscapes made by Bernd and Hilla Becher and the study of social types made by August Sander; and through my own experience of the street food and outdoor markets of Thailand. However, to highlight such constraints did not seem enough. So I created a white, unmarked model of a typical Toronto restaurant façade (formerly a shop front.) This tabula rasa suggests the possibility for an alternative strategy by showing the limitations of the channels through which we are forcing cultural diversity. The blank shop front model brings us back to a starting point from which cultural diversity can be reconsidered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.060 | 0.026 |
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".