Bibliographic record
Abstract
Staring at the U.S. recession of 2008-10 and at the euro crisis of 2010-12, it is tempting to look for common features. In both Los Angeles and Madrid, house prices more than doubled between 2000 and 2008, and household debt increased in tandem. Both in the United States and in southern Europe, total public debt reached historical levels, and during the two crises, yields on state debt increased remarkably as did the price of credit default swaps insuring against default. A story of the crises across the two sides of the Atlantic that is based on leverage and debt is both appealing and superficially correct. However, a closer look at the data in the two regions leaves too many questions open. The increase in house prices was not uniform across Europe (or the United States), with large movements in Ireland and Spain, but relative stagnation in Portugal and Italy, and only moderate increases in Greece, yet all of these regions went through a sovereign debt crisis. The increase in public debt was at the federal level in the United States (while at the state level in Europe), yet the American sovereign debt problems happened exclusively in a few states, California and Michigan more noticeably. Arellano et al. (2015), henceforth AAW, add a further comparison that makes a simplistic leverage story even harder to take at face value. Look at Canada. House prices also almost doubled in the first decade of the XXIst century, and private leverage followed suit. But prices have neither fallen (at least ∗Contact: rreis@columbia.edu. I am grateful to Cynthia Balloch, Keshav Dogra and Savi Sundaresan for useful discussions.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.057 | 0.053 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".