Some States Stepping In: Politics and Discourse in Foreclosure Prevention Legislation Outcomes During the Financial Crisis
Bibliographic record
Abstract
At the core of the 2008 global financial crisis was a foreclosure crisis in the United States. The federal government focused on responding to the concurrent banking crisis, leaving foreclosure prevention to the states. Despite a nationwide crisis, only some states advanced foreclosure prevention policies. Political theory scholars argue that political ideology and economic interests are the primary drivers of policy outcomes, while discourse scholars argue that themes in the public discourse shape policymaking. In this article, I integrate these literatures to develop and test an account of foreclosure prevention policymaking. To measure discourse, I scrape the text of over 20,000 state-level media publications and inductively code them using Structural Topic Modeling. Using event history analysis, I examine the relationship between discourse themes, political factors, and the timing of foreclosure prevention policymaking by state legislatures. I find that states where a “markets” theme was more prevalent in the foreclosure discourse were less likely to advance foreclosure prevention policies, whereas states with discourse focused on “intervention” were more likely to do so. Results also corroborate previous scholarship showing that political ideology and special interest group activity impacted these policy outcomes.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".