Tracking Group Quarter Facility Data: The Washington State Experience Applied Demography Conference
Bibliographic record
Abstract
Program (SAEP) began publishing population and housing data for sub-county areas such as school districts, legislative districts, census tracts, and census block groups starting in December 2005. The SAEP draws data from OFM’s official April 1 city/county population estimate program and distributes it to census blocks based on geocoded point data such as group facilities locations, housing starts, and polygonal data such as postal delivery statistics. Group quarters (GQ) are particularly important to both the city/county estimate program and the SAEP because changes in GQ populations can have a dramatic effect on local area estimates. Group quarters data are inherently difficult to maintain. When facilities change ownership they are frequently renamed; they may merge with other facilities; and they can open or close just like any other business or institution. Facilities which serve multiple functions may mix GQ and non-GQ populations such as when nursing homes also serve as rehabilitation centers. The licensing of adult family homes frequently results in a change in use from “traditional ” residential housing to small GQ facilities, muddling the accounting of both household and GQ populations. Group facilities are also surprisingly difficult to locate using a geographic information system. Administrative addresses are often reported for college dormitories, prisons, and other large institutions. The administrative offices are sometimes located far enough away from the physical location of GQs that the populations are incorrectly assigned to the wrong census tracts or blocks. This paper focuses on how OFM collects, manages, and maintains GQ data for the city/county and SAEP programs. The primary topics addressed include data quality, data integrity, geocoding, the handling of multipart facilities, and various data elements that have proved useful in the estimation process. 2
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".