Preventing a Return Engagement: Eliminating the Mortgage Purchasers' Status as a Holder-in-Due-Course: Properly Aligning Incentives Among the Parties
Bibliographic record
Abstract
that they have made in the foreclosed property.These individual tragedies are hard to understand in the abstract, yet recitation of individual cases provides no true picture of the scope and depth of the problem created by foreclosed mortgages. 3Suffice it to say, the human costs are incalculable, and the impact that these foreclosures will have on families will reverberate throughout American society for at least the next generation.Just as importantly, on a macro level the impact of the huge number of individual real estate foreclosures on credit and financial markets has been catastrophic leading to the demise of venerable financial institutions, 4 the taxpayer-government financed bailout of banks and other financial institutions,' and the collapse (deflation) of the stock market. 6 In short, the demise of the residential real estate market (what I term the "foreclosure miasma") has caused the American economy to enter into the longest recession since the Great Depression of the 1930s. 7Doom and gloom abound and there is no end in sight.Furthermore, the finger-pointing has begun.What has caused this calamitous state of affairs, and, to a lesser extent, what can be done to Accelerates, CENTER FOR ECON.& POL'Y REs., Feb. 25, 2009, http://www.cepr.net/index.php/data-bytes/housing-market-monitor/housing-price-decline-accelerates(data demonstrates housing prices were falling over 20% in the last quarter of 2008); David Goldman, Housing Prices to Free Fall in 2008, CNNMONEY.COM, http://money.cnn
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".