Oil Vulnerability in the Greater Toronto Area: Spatial Analyses of Socioeconomic Risks from Higher Urban Fuel Prices
Bibliographic record
Abstract
The rising cost of fossil fuel is a recognized phenomenon, but its impact at the household level is still widely unknown. Understanding how the socioeconomic impacts of rising fuel prices might be distributed across urban areas is a critical issue that is necessary for sustainable urban transportation planning. This study has refined the vulnerability index for petrol expense rises (VIPER) framework previously proposed by Dodson and Sipe (2007) by incorporating travel survey data to better represent households’ car dependence. Through this modified VIPER framework, the authors seek to understand how the socioeconomic impact of rising fuel costs will be distributed across the Greater Toronto Area (GTA). The findings of this research reveal a pattern in the distribution of oil vulnerability that depicts a three-ring configuration: expanding outwards from the lowest oil vulnerability in the urban core (1st ring), followed by the highest oil vulnerability in the city’s inner suburbs (2nd ring), and a transition to a lower oil vulnerability in the suburban areas (3rd ring). Such results reveal the need for transportation and land use policy measures that tackle transportation-related social exclusion due to high fuel prices in the future.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".