Dynamics of the Location of Financial Institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cities are a significant source of economic growth and prosperity, but they may also contribute to social and economic problems, including unemployment, poverty, and inaccessible financial institutions. The authors have gathered a unique panel data set for Toronto that locates financial institutions by census tract and links this information to census public use microdata from 1981 to 2006 to show that mainstream financial institutions have migrated to the suburbs and that, simultaneously, so-called fringe financial institutions, especially payday lenders, have expanded their operations in the inner city. The authors then use panel regression models and, among other results, find that census tracts with low income are less attractive to mainstream institutions over time and more attractive to fringe institutions, which provide more limited and expensive services. The results imply that the dynamics of the location of financial institutions may present an additional barrier to upward economic mobility for inner-city residents.
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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 it