Bibliographic record
Abstract
Providing decent, safe, and affordable housing to low- and moderate-income families has been an important public policy goal for more than a century. In recent years there has been a clear shift of emphasis among policymakers from a focus on providing affordable rental units to providing affordable homeownership opportunities. Due in part to programs introduced by the Clinton and Bush administrations, the nation's homeownership rate is currently at an all-time high. Does a house become a home only when it comes with a deed attached? Is participation in the real-estate market a precondition to engaged citizenship or wealth creation? The real estate industry's marketing efforts and government policy initiatives might lead one to believe so. The shift in emphasis from rental subsidies to affordable homeownership opportunities has been justified in many ways. Claims for the benefits of homeownership have been largely accepted without close scrutiny. But is homeownership always beneficial for low-income Americans, or are its benefits undermined by the difficulties caused by unfavorable mortgage terms and by the poor condition or location of the homes bought? Chasing the American Dream provides a critical assessment of affordable homeownership policies and goals. Its contributors represent a variety of disciplinary perspectives and offer a thorough understanding of the economic, social, political, architectural, and cultural effects of homeownership programs, as well as their history. The editors draw together the assessments included in this book to prescribe a plan of action that lays out what must be done to make homeownership policy both effective and equitable. Contributors: Eric S. Belsky, Harvard University; Charles C. Bohl, University of Miami; Rachel G. Bratt, Tufts University; J. Michael Collins, Policy Lab Consulting Group, LLC; Walter Davis, Statistics New Zealand; Mark Duda, Harvard University; Avi Friedman, McGill University; Edward G. Goetz, University of Minnesota; Roberto G. Quercia, University of North Carolina at Chapel Hill; Carolina Katz Reid, Federal Reserve Bank of San Francisco; Nicolas Retsinas, Harvard University; William M. Rohe, University of North Carolina at Chapel Hill; Michael A. Stegman, John D. and Catherine T. MacArthur Foundation; Lawrence J. Vale, Massachusetts Institute of Technology; Shannon Van Zandt, Texas A&M University; Harry L. Watson, University of North Carolina at Chapel Hill
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".