Counting everyone: evidence for inclusive measures of disability in federal surveys
Bibliographic record
Abstract
The US Census Bureau has used the American Community Survey six-question set (ACS-6) to identify disabled people since 2008. In late 2023, the Census Bureau proposed changes to these questions that would have reduced disability prevalence estimates by 42%. Because these estimates inform funding and programs that support the health and independence of people with disabilities, many disability researchers and advocates feared this change in data collection would lead to reductions in funding and services. While the Census has paused-but not ruled out-the proposed changes, it is critical that alternate, more inclusive disability questions be identified and tested. We used data from the 2023/2024 National Survey on Health and Disability to explore alternative questions to identify disabled people in national surveys. A single broad question about conditions identified 11.2% more people with disabilities, and missed significantly fewer people with psychiatric disabilities compared to the current ACS-6 questions. A combination of a broad question and the existing ACS-6 questions may be necessary to more accurately and inclusively identify people with disabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".