“Second Tier Cool”: Residents’ Experiences of a Mid-Size City’s Gentrifying Downtown
Bibliographic record
Abstract
Ubiquitous depictions of life in a big city evoke images of young professionals enjoying craft beers on patios, eating out at trendy bistro-lounges, and biking on dedicated cycling lanes to work. Positive portrayals of downtown living in mid-size cities however, are much more uncommon, and there is little in the literature that discusses the revitalization of these smaller urban centres. This research begins to fill the gap by analyzing the gentrifying processes of mid-size cities’ downtowns, using the City of Kitchener, Ontario as a case study. Through observations, census data analysis, and interviews, the study addresses how residents’ experiences of the downtown reflect both the gentrification literature and Kitchener’s downtown plans. This research found that participants’ experiences of living in the core revealed the distinctive upgrading trajectory of this smaller city and reflected elements of Kitchener’s urbanity, the downtown’s decline, and its mid-size status.
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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 | 0.008 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".