Bibliographic record
Abstract
Chapter 7 details the retrenchment of German housing programs during the country's structural economic crisis in the 2000s. Unlike American policymakers who expanded housing programs during the 2008-2009 crisis, German leaders cut housing programs to reduce fiscal deficits and reallocate funds to education, research, and technology. Following reunification, Germany experienced a brief housing boom in the 1990s, driven by demand-side housing stimulus programs, including a mortgage interest deduction, to spur growth in eastern Germany. However, this boom soon turned into a construction bust, leaving the country with one million vacant homes and reinforcing mass unemployment and capital misallocations in the economy. For German policymakers, housing programs became structural economic problems detrimental to the manufacturing-based, export-oriented economy. In 2006, Chancellor Angela Merkel's grand coalition sacrificed major social housing and homeownership programs, despite their popularity, in the name of reviving the German export-oriented economy.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".