Lofts in translation: Gentrification in the Warehouse District, Regina, Saskatchewan
Bibliographic record
Abstract
Abstract This paper contributes to analyses of gentrification in smaller cities. Drawing on a case study of Regina, Saskatchewan's Warehouse District, I document the conversion of post‐industrial structures into loft condominiums and the rise of middle‐class consumption to support this residential base. I demonstrate that while the Warehouse District displays similarities with the paradigmatic case studies on loft living that dominate the literature (e.g., demographics, built form, gentrification), it also highlights how these processes and practices are adapted in peripheral spaces. While the resemblance to the loft scene in SoHo, New York is evident in loft districts internationally, it is less relevant in this smaller urban context. Ultimately, the chic cosmopolitanism of loft living is lost in translation, leaving an incoherent place identity as producers and consumers attempt to engage with global and local ideals of the loft habitus. This paper draws on a qualitative approach including semi‐structured interviews with loft residents, local businesses, and key stakeholders, and discourse analysis of planning and policy materials and media documents.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".