Bibliographic record
Abstract
This study will determine how the student population living off campus in Kingston has expanded from 2006 to 2016 by examining census data. Census data shows us the number of unoccupied dwellings which can represent where students are living in cities because full-time students living away from home are attached to their parents’ residence in the census. A larger scale study has been conducted for unoccupied dwellings in Ontario mid-sized cities with Universities’, Kingston being one subject. This previous study shows a disproportionate increase in student population at Queen’s to the increase in on campus housing, resulting in more students living off campus in Kingston (Lauzon 2021). This research project will examine how the areas where students are living in Kingston has expanded and changed by analyzing the dissemination areas in the census tracts around Queen’s University and St. Lawrence College. This expansion represents a process known as “Studentification” which refers to the increase of higher education students occupying neighbourhoods, and the affect it has socially and economically on communities (Smith 2005). The outcome of this research project will be a report summarizing findings from the data analysis, limitations of the research method and a digital atlas mapped using a Geographic Information System (ArcMap v10.5) showing the physical change to the University district and surrounding areas in Kingston. References Lauzon, M. 2021. “Where did the Neighbourhood Go? A Look into The Spatial Distributions of Student Housing Across Ontario Mid-Sized Cities”. Master’s Report, Queen’s University School of Urban and Regional Planning. http://hdl.handle.net/1974/28835 Smith, D. P. 2005. “‘Studentification’: the gentrification factory.” Atkinson, R. Bridge, G. (eds.) Gentrification in a global context: the new urban colonialism. Housing and Society Series, Routledge, UK, 72-89. https://doi.org/10.4324/9780203392089
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".