Cartographic Editorial—Mapping the Racial/Ethnic Topography of Subprime Inequality in Urban America
Bibliographic record
Abstract
The worst global financial crisis since the Great Depression—a wide-ranging, multidimensional catastrophe often labeled the Great Recession—began to unfold in late February 2007 as bond-ratings analysts in New York, London, and Hong Kong reviewed the latest quarterly disclosures on securities backed by millions of subprime mortgage loans made to borrowers in cities and suburbs across the United States. For many years, the high-cost, high-risk subprime market had flourished by exploiting the interdependent American inequalities of race, ethnicity, class, and place: subprime lending was disproportionately focused on racially and ethnically marginalized people and places (Bradford, 2002; Squires, 1992, 2003) and extracted profits from local, place-bound housing transactions to provide revenue streams for local brokers and lenders and investment opportunities for large national and transnational banks, Wall Street investment firms, hedge funds and monoline insurance companies, and institutional investors around the world. At its peak, the lending boom comprised a substantial (if ultimately unmeasurable) proportion of the highly leveraged web of promises in the seemingly placeless, Castellian space of flows of a credit-default swaps industry estimated at more than $60 trillion. As the housing boom collapsed, high-yielding subprime securities suddenly became known as “toxic assets,” and investor panic brought a cascade of structural failures in the architecture of the global financial system. Big failures begat big bailouts: by the spring of 2009, the International Monetary Fund estimated total worldwide losses to financial institutions of $4.1 trillion, whereas in the U.S. alone more than a dozen interwoven initiatives authorized by Congress in the “Troubled Asset Relief Program” committed almost $3 trillion of public funds to the financial system (SIGTARP, 2009). Even more money came
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.151 | 0.038 |
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".