Building a Better Future: Solving Seattle’s Housing Crisis
Bibliographic record
Abstract
The City of Seattle, like much of the country, faces a serious housing crisis. Houselessness is ubiquitous and most ordinary citizens, many of whom were deemed essential workers during the pandemic, cannot afford to live in Seattle. Much of the workforce is forced to suffer financially or commute long distances, which weakens communities and accelerates climate change. One of the main policy responses to this, at least at the municipal level, has been to up-zone neighborhoods. The assumption behind liberalizing the housing market is that more supply will be generated and in the process the cost of housing will be within reach of more citizens. In short, it presumes that a less regulated housing market will increase supply which in turn will result in more affordable housing. Unfortunately, this is unsubstantiated. Researchers in Chicago and Vancouver have found that up-zoning has neither increased the overall supply of housing nor reduced its costs. Sustainably built, quality housing that all residents can afford is not a profitable venture. Thus, one of our main takeaways is that up-zoning based around for-profit housing development will not address the housing crisis. Moreover, many quality neighborhoods will be upended and up-zoning as a policy (which we believe is needed) will have been discredited because it failed to deliver what many of its advocates promised.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 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".