Building Stories: Critical Geography of Architecture and the Study of Everyday Practice in Detroit, Michigan
Bibliographic record
Abstract
In Loretta Lees's study of a new public library in Vancouver in the late 1990's, she began to explore the ideals of non-representational theories, or those everyday practices that provide evidence not just of what symbolic meaning one may assign to a space, but rather what that space does—how it is enacted through everyday practice. This exploration provided Lees with another way to think about the built environment, one that she believed could open up a new direction for architectural geographers. Lees, building on the work of Jon Goss and other contemporary scholars in the field, described this new direction as a move towards a critical geography of architecture. This dissertation explores the use of a non-representational framework to study everyday practices through a single case study in the Avenue of Fashion in Detroit, Michigan. This research considers the historical evolution of Detroit through bankruptcy to present day using two common narratives of the city, one of rise/rebirth and one of Two Detroits, to offer a critical lens through which to consider performances of everyday life in this recently redeveloped area of the city. Within a non-representational framework, this study pulls in direct observational methods such as counting, mapping/tracing, photo documentation, trace observation, and field notes derived primarily from public life studies to observe and consider how the built environment is shaped through these embodied practices. This study contributes both an example of alternative methods that may be used in non-representational research, as well as new way to think about spaces that complements findings from more representational research. The findings from this study inspire a curiosity about the unfolding of everyday life and contribute to the work of Lees and others in advancing a critical geography of architecture.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".