The Effects of Urban Sprawl on Poverty in the City from Which It Emanates
Bibliographic record
Abstract
Urban sprawl has been widely criticized in academia and other forums for the slew of aesthetic eyesores it has created and environmental issues it has exacerbated. Less discussion or research has taken up sprawl’s effects on poverty and how they intersect with class and race to self-propagate. In this paper, I gather findings from existing research on the subject and explain points of consensus and discord among scholars. The three ways by which urban sprawl worsens poverty are found to be spatial mismatch, the physical alienation of inner-city dwelling residents from suburban centres of concentrated employment; income segregation, the relegation of lower income residents to places of low job-concentration; and racial segregation, the spacial sorting of residents by race so that racial diversity decreases in given areas of residence. It is also found that these factors, which contribute to poverty and are driven by sprawl, also then drive sprawl themselves, creating feedback loops intensifying segregation and poverty. The inception of the modern city and the process of urban sprawl are also discussed. It is found that the same processes which created high population concentration to create the first cities during the industrial revolution, notably the advent of labour centres, drive modern urban sprawl. This and other processes are then used in the examinations of the above-mentioned issues to calculate the origins of these issues and contextualize them.
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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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".