Community Changes and Growth in Small Cities: Resident Perceptions of Growth in Brandon, Manitoba, Canada
Bibliographic record
Abstract
Abstract While research on rural depopulation and urbanization to large cities has dominated the literature for decades, small city growth has been largely ignored. Yet, small cities are important to the regional systems in which they are located, including serving as centres to their rural surround. This paper is concerned about growth and change in small cities. Using Brandon, Manitoba as an example, citizen perceptions of a range of specific aspects of city growth are analyzed. The study is based on a household postal survey conducted in May and June 2010 in which 2,500 randomly selected households in Brandon, Manitoba received questionnaires. The results are based on the 518 useable, completed questionnaires that were returned. Differences in perceptions of the pace of change in a range of aspects of development were found based on income, age, educational attainment, employment status, and home ownership. No differences were found based on gender. The paper concludes with comments about how city governments need to be aware of citizen perceptions when pursuing and managing development. The paper also illustrated the importance of citizen perceptions in understanding the pace and direction of change in small cities.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".