Poor Black Woman. The Surveillance of Class, Race, and Gender in Canadian Urban Planning: An Autoethnography
Bibliographic record
Abstract
Leveraging my own lived experiences, this thesis unpacks how Ottawa Community Housing Corporation (OCH) has quietly reorganized its parking practices to further surveil predominantly racialized and precarious individuals in their communities.By using a mixed-methods, ethnographic approach that includes autoethnography, participant observation, interviews, and online archival work, I argue that this OCH parking regime is an extension of historical urban planning processes in Canada.One that reinforces racialized dispossession and oppression, and that this parking system feeds racialized bodies into broader global surveillance systems.Through the lens of class, race, and gender, I provide a historical analysis of how each identity we intersect with increases the likelihood that we will not only live in poverty but become entrapped in systems that work closely together to survey, control, and punish us based on social markers.By the end of this thesis, readers should have a clear understanding of how the position that Black peoples find themselves in today is not by chance but a built-in feature of white supremacist systems, and what it means to be a poor Black woman in Canada. "I'm from the mud. I cannot afford to lose focus." -ShayboWith love and humility, I would like to thank all those who gave so much to get me to this point.My hooyo (mother) and aabe (father) who instilled a deep sense of integrity in my siblings and I and immersed us in advocacy work from a very young age.Something that has defined every decision that I have made throughout my life.My older sister Faduma who has always been my rock and has kept me anchored.I could never repay you for all the sacrifices you've made.I'm grateful that during our lifetime we went from supporting each other through traumas to building a future together that is filled with joy and unlimited imaginings.My siblings (Lukman, Bukhari, Raja, Abii, Omar, Jim, Abdifitah, Asha, and Ayan) who have always supported the moves I've made, even if they did not fully understand it.My mother's siblings especially habaryar Zahra who would sign the back of her cheques and give it to my hooyo.She even went so far as to pawn some of her gold jewelry and give the money to
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.047 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".