Transportation Related Social Exclusions and Homelessness: What Does the Role of Transportation Play in Improving the Circumstances of Homeless Individuals?
Bibliographic record
Abstract
Mobility has the potential to improve the quality of life of vulnerable population segments in the society. A typical example of this is providing opportunities for homeless individuals to break free from the cycle of poverty and homelessness. Transport-related exclusion of homeless individuals is a catalytic factor for the homeless population segments in large metropolitan areas. This paper looks into the issue the role of urban transportation on state and level of complexities of homeless individuals in large urban centres by taking the City of Toronto as the study area. The research relies on a specially designed interview instrument, which encompasses both revealed preference and stated adaptation questions on travel behaviour of a sample of homeless indivduals in the City of Toronto. The qualitative results of the research identify the intricacies of transportation related social exclusions and potential to come out of the cycles of poverty and homelessness. It is clear that the relationship between transportation related social exclusions and homelessness is complex and the role of transportation on social exclusions of homeless individuals heavily depend on individual’s personal experiences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".