Weathering the storm: Generating intersectional urban design understandings for winter cities
Bibliographic record
Abstract
Taking an intersectional approach, this research explicates the unique manner in which spatial injustice is experienced in a winter city when an individual possesses the multiple disadvantaged identities of disability, gender, age, and class. Employing case study methodology and go-along interview methods, this research answers the question: how can the lived experience of an older, formerly homeless woman with mobility and mental health disabilities inform intersectional design recommendations for winter cities? The findings identify three priority areas for intersectional design in winter cities to facilitate inclusion, wellness, and resilience among those disadvantaged by disability, gender, age, and class. These areas are: components of the built environment requiring intersectional understanding of accessibility (sidewalks, public transit-access routes, building entrances, and public transit pick-up zones); the urban context of senior and affordable housing; and public transportation. This paper contributes to the literature by demonstrating that intersectional understandings of urban winter environments are potent knowledge towards transforming cities from ones that disable and marginalize, to ones that enable and empower.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".