Undoing White Settler Designed Cities: The Agency of Mapping with Racialized Immigrant and Refugee Women in Canada
Bibliographic record
Abstract
Although urban populations are becoming increasingly diverse, most cities are not designed to provide equitable access to urban amenities and infrastructure. Twentieth- century Western urban design standards were rooted in Eurocentric ideals, primarily addressing the needs of White, economically secure, able-bodied, neurotypical, cis-gender, heterosexual males. As a result, one key aspect of designing equitable cities is understanding the different embodied experiences of marginalized populations. However, at present, city planners rely on quantitative and abstract urban studies that continue to render other social groups invisible. This issue is particularly relevant in countries such as the settler state of Canada, where projections estimate that by 2041, one in three people will be a current or former immigrant. In addition, two in five people and one-third of the total female population will belong to racialized populations. This study focuses on Ottawa-Gatineau, two neighboring cities that mirror national demographic trends. I combine two mapping methods to document the distinct urban experiences of diasporic communities. The first method involves using Geographic Information System (GIS) to map census data, creating demographic maps of Ottawa-Gatineau. The goal is to select neighborhoods with a high density of economically insecure and racialized immigrant and refugee women, where the study of urban equity is more relevant. The second method involves a participatory mapping workshop to document the first-hand urban experiences of community members. The goal is to assess the adequacy and accessibility of urban infrastructure in their neighborhoods. Participants overwrite a map with their comments as an empowering technique that emphasizes their capacity to lead changes. The assessment, driven by the interests of participants, addresses topics such as transportation, amenities, services, and housing. The study reveals that beyond mere physical presence and proximity, questions about cultural, religious, and linguistic diversity, gender and age inclusivity, safety, affordability, schedule flexibility, maintenance, transit reliability, and social diversity and connectivity were crucial in assessing the adequacy and accessibility of urban infrastructure.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.058 | 0.014 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".