At the Intersection of Equity and Innovation: Trans Inclusion in the City of Vancouver
Bibliographic record
Abstract
In 2016, the Vancouver City Council passed the Supporting Trans* Equality and an Inclusive Vancouver policy, a motion that prompted the development of a strategy aimed at ensuring the safety and accessibility of municipal programs, services, and physical spaces for Two-Spirit, trans, and gender-diverse (TGD2S) users, including residents, City staff, and visitors. Binary gender is a taken-for-granted assumption of most urban forms and functions: It is encoded in all municipal data collection forms, building codes, signage, and communication strategies. At its root, then, addressing trans inclusion requires the municipal government to attend to and redesign the gendered models of service, programs, and space upon which the city is built. This article tells the story of the Supporting Trans* Equality and an Inclusive Vancouver policy and is driven by two goals. First, I document this policy as a contribution to the urban policy and planning literature, where attention to gender diversity is due. Second, using the trans inclusion strategy, I show how a municipal equity policy aimed at addressing the safety and inclusion of TGD2S people can have significant impacts beyond its immediate scope. To develop this idea, I consider how equity-driven innovation can substantially reshape institutional practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.025 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| 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".