Putting Housing Back on the Political Agenda
Bibliographic record
Abstract
I come here today neither to praise nor to bury the U.S. Department of Housing and Urban Development (HUD), but to provoke a discussion about why housing issues and housing policy are not on America’s political radar screen, and what we can do to put them there. HUD—or at least HUD during the Clinton Administration under Secretaries Henry Cisneros and Andrew Cuomo—surely deserves a great deal of praise. First and foremost, it has survived. It was not so long ago that some powerful political forces did, in fact, want to bury HUD. The department was on the political chopping block, a target of vicious criticism. In October 1996, the New York Times magazine published a cover story by Jason DeParle entitled, “The Year That Housing Died,” claiming that “the Federal Government has essentially conceded defeat in its decades-long drive to make housing affordable to low-income Americans ” (DeParle, 1996). In 1994, House Speaker Newt Gingrich said that, “You could abolish HUD tomorrow morning and improve life in most of America ” (Cooper, 1994). A year later, the Washington Post reported that, “Politically, HUD is about as popular as
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".