Resisting Erasure: Forging Our Own Space and Histories
Bibliographic record
Abstract
The idea that queer communities, due to their marginalized state, are inherently accepting of all identities regardless of race, gender, culture, and religion is extremely flawed. Racism and discrimination based off one’s identities are commonly experienced within LGBTQ2IA+ community, placing queer and trans, Black, Indigenous, and more people of colour (QTBIPOC) in a vulnerable position forcing them to seek out and forge spaces where their identities feel welcomed and valued. This major paper and the accompanying film contributes to these discussions by exploring the spatial accounts of queer racialized people who are living, working, playing or participating in activism in Toronto’s Church-Wellesley area, also known as The Village. This major paper and film also includes an analysis looking at the impacts of planners and the field of planning on how queer racialized people experience queer space; putting forth a perspective that is absent from the practice, including the curriculum. This major paper therefore provides an argument for the need to reconstruct how spaces are formed, whilst beginning to underscore the inadequacies of the system(s) which planning and adjacent city-building professions operate under.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.045 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".