Growing community shaping place: A community centre and garden for refugees settling in Winnipeg
Bibliographic record
Abstract
As refugees settle into life in a new country they learn the local customs and culture, but for a successful integration the host society must also adapt. In exploring the complexity of the settlement process and the important role that social networks play in refugee integration, this project proposes to design a community kitchen, garden and café – a cultural hub for recently arrived refugees and long-term locals alike. By engaging with urban agriculture as both a placemaking tool and a form of food subsidy, gardening practices provide a foundation for thinking about barriers to settlement and strategies in community engagement. The proposed design is a renovation to an existing building in Winnipeg. The space aims to foster a sense of place by promoting both participants’ feelings of ownership over elements of the program, and by creating an environment that welcomes the community. In considering the physical design of the space, theoretical conceptions of place, identity and sense of community belonging will inform strategies for designing for people in transition, and simultaneously, for long term connections. The result will be a design that respects diversity, fosters community, and that positions itself as a model for ways of living in the city.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".