Addressing the need for more nuanced approaches towards transit‐induced gentrification: A case for a complex systems thinking framework
Bibliographic record
Abstract
Abstract The role of public transportation has shifted over the last 2 decades as planners and policymakers increasingly integrate new transportation infrastructure as an economic growth tool that promotes density and desirability. This shift has also positioned new infrastructure as a driver for neighbourhood change and gentrification, leading to the evolution of literature that explores transit‐induced gentrification . As this scholarship grows however, research has become fragmented, as the political economy work, which frames much of gentrification, is antipathetic to the neoclassical perspective that frames transportation research. The resulting inconsistencies have left researchers calling for the integration of new and holistic approaches that can address growing gaps. With transit‐induced gentrification becoming more prevalent across large and mid‐sized cities, and research lacking methodological consistency, this review considers: Can a complex systems thinking framework be used to better understand and address the process of transit‐induced gentrification?
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".