Cities for Seven Generations: Recognizing, Reconciling and Reimagining our past, present, and future in Canadian urban centres
Bibliographic record
Abstract
How do we make our cities better? A google search will return 2.6 billion results in half a second. A scan of those results reveals multiple ‘top 10’ lists—maybe an article on how to revitalize Main Street. These solutions lack systems-level interventions, which this research proposes is necessary to move beyond ‘top 10’ solutions that merely add a façade over inequitable systems and policy, and over infrastructure and resource-usage that damages our earth. This research focuses on disrupting how the interrelations of culture, race, gender, economics, and politics affect the level of benefit someone experiences in a system (e.g., city policy), and aims to challenge assumptions inherent in existing systems. \n\nThe current juncture of global social, economic, and environmental crises offers a unique opportunity to rethink how we live, and to reconsider the design of our urban centres. Previously, I reimagined a city block in Vancouver, designing a carbon-neutral women’s shelter and social enterprise that shared economic resources, social supports, and ‘green’ energy with an adjacent Longhouse. My current PhD research imagines the impact of this synergy at a city-wide scale—What if buildings included free social purpose space? What if adjacent structures shared green energy infrastructure? What if…?\n\nThis research will co-create a decision framework enmeshed within Indigenous worldviews to offer a way to reimagine our cities. This framework (“Cities for Seven Generations Model”) is based on four key social and worldview concepts: place, language, governance, and social cohesion. This research will focus on commonalities across multiple worldviews, and research alongside urban Indigenous communities to co-design localized adaptations. The Cities for Seven Generations Model prioritizes Indigenous worldviews and ways of knowing while being compatible with western ones. It will inform cooperative, culturally-appropriate, and diverse approaches towards equitable decision making in city planning, governance, policy, and resource allocation.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.065 | 0.026 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".