Infra-sutures: New Perspectives in Responsive Design and Community Engagement
Bibliographic record
Abstract
Whether considering the construction of highways, urban renewal, or concentrated poverty, many low-wealth communities of color bear the scars of inequity. These scars are symbols of racial and socioeconomic trauma with deep physical, cultural and economic impact; however, this plan asserts that if the built environment has the power to entrench and reinforce hierarchies, it also has the power to participate in dismantling oppressive ideologies and advancing racial and socioeconomic equity. With Richmond, Virginia serving as the case example, this plan proposes a community engagement process and culturally responsive design principles to activate public space redevelopment projects as infra-sutures. Developed by dlandstudio in Montreal, QB, Canada, infra-sutures conceptualizes efforts to reconnect communities disrupted by transit infrastructure in Montreal. This plan builds upon this concept by broadening infra-sutures to include reconnecting communities culturally and economically for healing and restoration. The four phases of engagement to redevelop public spaces as infra-sutures include: pre-planning (co-learning and sharing power with residents); inclusive planning (residents as the anchor); planning for racial equity through design; and implementation and stewardship. Each phase is built on a foundation of core beliefs that race has shaped the built environment; equitable revitalization should lead to cultural and economic wealth building; the process matters just as much as the completed project; and the expertise of residents should shape communities. This plan combines research from literature and interviews with Richmond residents, historians, and community engagement experts to develop an engagement process aimed at advancing racial and socioeconomic 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 | 0.050 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".