Diving into Debt: A Study on Factors Related to Debt Risk Score in Toronto
Bibliographic record
Abstract
This study aimed to find the correlations between data found regarding debt risk and the 140 neighbourhoods in Toronto, Ontario. Debt risk was compared with select variables from available data, including education, health, environment, housing, economics, demographics, transportation, recreation, and safety. The purpose of this study was to help civilians and the government identify possible factors that lead to higher debt risk, as well as find solutions to reduce it. The data was retrieved from Open Data Toronto. A simple linear regression model was built to determine the factors that have a seemingly great correlation with debt risk. It was concluded that the percentage of people who receive social assistance, the percentage of people who applied for rent banks, and the number of reported sexual assaults in a neighbourhood had a positive correlation with increased debt risk. The result is that an age-adjusted rate of people who received breast cancer screening had a negative correlation with increased debt risk. Through the results, several solutions could be proposed to reduce debt risk. More education on safety and health can enable citizens to become more responsible and aware of their financial state. Giving other forms of aid that are not monetary may be beneficial in helping people get out of debt and become more financially independent.
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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".