Unravelling neighbourhood change: Decomposing the effects of residential mobility and incumbent change on credit access in California
Bibliographic record
Abstract
Neighbourhood change is a complex and dynamic process stemming both from macro-level changes in economic conditions and from micro-level changes in circumstances of individual residents. This article explores the intersection of these contributions to processes of neighbourhood change through the lens of changes in consumer credit access in California during the late 2010s. While improvements in consumer credit access were widespread across California neighbourhoods during this period, the specific mechanisms underlying these neighbourhood changes are found to differ in important respects. Employing credit panel data, this article identifies the distinct contributions of residential mobility and incumbent change to changes in consumer credit access at the neighbourhood scale. Descriptive and cluster analysis further reveals the types of neighbourhoods in which these mechanisms operate, highlighting the dominant role of incumbent credit score growth in neighbourhoods with moderate incomes and racially diverse populations with high rates of homeownership. The findings of this analysis point to the importance of uncovering the mechanisms underlying neighbourhood change, and indicate the existence of multiple such mechanisms facilitating the widespread expansion of access to consumer credit in California during the late 2010s.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".