Emerging anti-poverty infrastructural gaps in suburbia: Poverty and the voluntary sector across Metropolitan Sydney
Bibliographic record
Abstract
Suburbs are subject to numerous stereotypes, including that they lack density, diversity and inclusivity. While these stereotypes have largely been dispelled, the deficit around anti-poverty infrastructure remains understudied. The focus of this paper is to systematically investigate the ostensible mismatch between (a) the emerging suburbanization of poverty, and (b) the potential lack of anti-poverty infrastructure to serve it, with a focus on suburban voluntary sector provision. These aims address the potential infrastructural deficit around voluntary sector provision in suburban areas of prosperous global cities in the Global North. Using Metropolitan Sydney as the case study, we investigate the extent of the suburban infrastructure service deficit across metropolitan space in 2016, comparing poverty patterns and supply of voluntary sector organizations. We find that poor inner and outer suburbs featured fewer services than the inner city, both per capita and per low-income residents, confirming an anti-poverty infrastructural gap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".