Archive of Data on Disability to Enable Policy and Research: Creating a Common Resource for Disability and Rehabilitation Stakeholders
Bibliographic record
Abstract
The Archive of Data on Disability to Enable Policy and research is a new ICPSR initiative to build a central repository of quantitative and qualitative data about disability that has been dispersed across disciplines. The mission of ADDEP is to improve and enable further research on disability for researchers, policymakers, and practitioners by acquiring, enhancing, preserving, and sharing data. This poster will display ADDEP's newly launched website and available resources. Also described in the poster are ways to discover data available to download from ADDEP and how the data can be used to better understand and inform the implementation of major disability-related policies such as the Americans with Disabilities Act. Details about how user-friendly data exploration tools and other resources on the ADDEP website will help to break down barriers to research within the cross-disciplinary disability and rehabilitation research community will be highlighted.
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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 | 0.042 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.082 | 0.026 |
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".