Power, Architecture, Transition: Creating a Safe Space for Victims of Domestic Violence.
Bibliographic record
Abstract
This thesis examines issues of poverty and homelessness in Toronto, specifically focusing on the needs of women and children who are the most vulnerable group and are homeless as a result of being victims of domestic violence. The thesis reflects on the power of architecture, relating to the limits of a physical environment created by an institution and how this effects rehabilitation and empowerment for shelter residents. This is a polemical thesis which creatively engages in the discussion of how informed design paired with an enlightened service model can create a positive implication on residents’ recovery. \n \nThe traditional and institutional notion of the shelter, with its objective of correction, is not capable of extending beyond offering accommodation, to address the questions of fundamental concerns to our society. Violence against women is a crime that exists in secrecy. Survivors of domestic violence remain invisible, without a visible place to speak, without a place to tell their own stories. Dialogue is transformative. Telling invokes transformation.(i) In this context, a shelter can become a space of resistance. \n \nThis thesis proposes a model for designing a shelter that is based on transformation, rather than adaptation. A model that openly instills invention and dialogue. A model that can question the relationship between personal and public. The aim of this project is to allow for architectural affordance created through affect and syntax. By looking at program possibilities, such as thresholds and gradients of privacy, this thesis proposes an approach that mediates the relationships between shelter residents, their community, and the surrounding neighborhood.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".