Planning Near-University Neighbourhoods: A case study of Kingston, ON and Ithaca, NY
Bibliographic record
Abstract
Neighbourhoods near universities, especially those with campuses near the centre of town, merit study for a number of reasons. High concentrations of students in central neighbourhoods and the distinctive housing and lifestyle preferences of students result in certain unusual conditions wherein an area may have low vacancy rates and high land values but declining housing quality and tension between students and permanent residents. Kingston, Ontario, and Ithaca, New York, are home to Queen’s University and Cornell University respectively and are both examples of small cities where a university campus and its attendant near-university neighbourhoods have a central and significant presence in the city. Kingston’s University District and Ithaca’s Collegetown have been the subject of recent urban design studies, and there was a clear opportunity to evaluate these neighbourhoods and their plans. The urban plans for both case studies demonstrated an awareness of the challenges faced by their respective neighbourhoods, and their recommendations would result in greatly improved near-campus areas according to most evaluation criteria. It should be noted that Collegetown is significantly more developed than Williamsville. As such, Kingston faces a much greater gulf between existing conditions and the idealized conditions presented in the Williamsville study, whereas the Collegetown Urban Plan contains significant but mostly incremental improvements in the form of infill and redevelopment of key areas.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".