Bibliographic record
Abstract
Since 1970, many inner-city neighbourhoods that were the domain of low-income groups occupying cheap, dilapidated housing have attracted higher socio-economic groups. As a consequence, capital invested has increased the condition and price of inner-city housing. This phenomenon is commonly called "gentrification." This thesis reviews the gentrification literature, analyzes gentrification within an economic framework, and uses regression analysis to test the following hypothesis: There is a lag between the first statge of gentrification, the start of demographic transition, and the second stage, rising real housing prices. An increase in real housing prices can, therefore, be predicted by observing which central neighbourhoods are beginning to undergo demographic change. The intra-urban gentrification model designed for this thesis regresses the change in real housing prices during the 1970s against the change in demographics during the 1960s. The sample is 95 inner-city census tracts from Vancouver, Ottawa-Hull, and Toronto. The conclusion from statistical analysis is that rising housing prices in gentrifying neighbourhoods can indeed be predicted by observing which inner-city neighbourhoods are starting to undergo demographic change.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".