Bibliographic record
Abstract
This paper is an ethnographic and sociological study of the neighborhood of Runnymede-Bloor West Village, identifying trends and drawing conclusions based on statistical data, academic theory, and notes taken during research trips. It is also worth noting that this study was conducted in January of 2020 before the Global pandemic was declared. Focusing on gentrification, segregation, and inequality, I identify that this neighborhood is part of a growing trend in Toronto of the increasing severity of all three of these issues. Runnymede-Bloor West Village is quickly becoming one of Toronto’s wealthiest neighborhoods, with the average household income increasing substantially. While this will certainly make real estate agents happy and will probably provide the city with more property tax, it also has the effect of pushing less affluent people out, as increasing living costs make their continued residence in Runnymede-Bloor West Village unaffordable. It also influences the local businesses, as businesses that do not cater to the new influx of affluent residents go out of business, either because their customer base has left or because they can no longer afford to pay their rent. I also identify the increased segregation of the neighborhood, as the racialized character of income inequality in Toronto results in people of color being priced out. Finally, I recommend that the solution to much of this increased inequality is the building of more affordable housing and restrictions of the building of unaffordable housing. Much of this will require the actions of a progressive, engaged local government. Hopefully, these steps will be able to halt or even reverse the trend of an ever-increasing cost of living, provide the local businesses with customers who do not have to spend most of their income on housing costs, and provide a short term solution to the issue of income and ethnicity-based segregation in Toronto.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".