A Granny Flat of Oneâs Own? The Households that Build Accessory-Dwelling Units in Seattleâs King County
Bibliographic record
Abstract
This paper inserts itself in current debates about the legalization of Accessory-Dwelling Units (ADUs), by casting a new light on the profiles of households filing ADU permits in the unincorporated areas of Seattle’s King County. Correlations between the concentration of minority households and the permitting of ADUs might call into question preconceived notions that such legalizations benefit suburban, older, white middle-class households in the first place. We seek to address the relationship between legalizing ADUs in King County, the major county of the Seattle metropolitan area, and general characteristics of households who build ADUs, based on age, race, and income. Findings underline premises for further evidence about the fact that minority homeowners benefit from the local permitting of ADUs. These findings could be the translation of a particular adequacy between ADU legalization and the long-term projects of local homeowners to transform their residential space.\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".