Housing Strategies for Growth in Neepawa, Manitoba: A Planning Perspective on Preparing for New Immigrants
Bibliographic record
Abstract
This thesis examines the housing demand pressures in light of growth due to immigration in the rural community of Neepawa, Manitoba. The town of Neepawa has been experiencing a significant increase in population, resulting from the recruitment of temporary foreign workers, arriving to work in a local pork processing facility. Access to housing has been identified as an important step for the integration of newcomers into their new communities. Many newcomers are remaining in the town after they apply for their permanent residency, often sponsoring family members to join them. Newcomers’ housing needs change with their situations. This research looks at this phenomenon from a community planning perspective. The research uses data from 10 semi-structured interviews with key informants, representative of real estate, government, immigrant settlement services, elected officials and industry sectors. The evidence suggests that the housing market within Neepawa has experienced significant change in recent years in light of changing demands in the market. The community has a need for housing that caters not only to newcomers but to an aging population as well. This study points to the need for more research that examines the housing experiences and trajectories of newcomers in rural communities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".