Exploring Trauma-Informed Practice and Equity in Vancouver’s DTES and Chinatown Programming
Bibliographic record
Abstract
This study was conducted following the Royal Roads Research and Ethics Policy, and in partnership with the Collaborative, a non-profit organization working to improve housing security and habitability for tenants living in Single Room Occupancy Buildings (SROs) in Vancouver’s DTES and Chinatown neighbourhoods. The research question was: How might the Collaborative explore the connection between trauma-informed practice (TIP) and equity in their Chinatown and DTES programming? The project methodology followed a systemic action research approach to apply systems thinking to the complex and relational issue of programming equity, together with members of the Collaborative. Interviews and conversation cafés were used to collect data from 20 staff and tenant participants of diverse intersectional identities. Seven key findings, five conclusions, and four recommendations were drawn, highlighting that the foundation of programming equity at the Collaborative is: (a) co-created policies and practices, and (b) connected relationships. Participants described equity as the iterative process of creating and revising shared agreements for program delivery that meet the needs of Collaborative members. This inquiry indicated that this iterative process is best supported through connected relationships, emotional regulation, acknowledging privilege, and systems thinking. The data also indicated that a sense of belonging can be used as a metric for equity.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| 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".