Bibliographic record
Abstract
This project explores the use of signage and place names in Toronto’s Financial District, focussing first on how signs are used in the district (with specific examples and a discussion of implications), and second, on how people refer to the district (that is, the words used to describe the area and the meanings and implications of those words). The research methodology includes direct observation and interviews of four subjects. This study reveals that several types of signs—both formal and informal—are prevalent in Toronto’s Financial District. Formal signs are those created by the government or businesses, and target both tourists and locals who work in the district. These signs suggest that the Financial District is an area for affluent workers and is supposedly a welcoming space for the international community (although English is the dominant language). Formal signs hint that those who work in the district are “successful,” and may encourage others to work diligently and aim for a career in the district. Informal signs are less frequently found in the area, indicating the high degree of control businesses have over the district. An analysis of word use reveals that the Financial District is commonly referred to as “Bay Street” by passersby, but is more likely to be called the “Financial District” by people who work there. This is examined in light of macro-level concepts such as globalization and group identity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".