Paperwork Reduction Act: New Approaches Can Strengthen Information Collection and Reduce Burden
Bibliographic record
Abstract
Testimony issued by the Government Accountability Office with an abstract that begins "Americans spend billions of hours each year providing information to federal agencies by filling out forms, surveys, or questionnaires. A major aim of the Paperwork Reduction Act (PRA) is to minimize the burden that these information collections impose on the public, while maximizing their public benefit. Under the act, the Office of Management and Budget (OMB) is to approve all such collections. In addition, agency Chief Information Officers (CIO) are to review information collections before they are submitted to OMB for approval and certify that these meet certain standards set forth in the act. GAO was asked to testify on the implementation of the act's provisions regarding the review and approval of information collections. For its testimony, GAO reviewed previous work in this area, including the results of an expert forum on information resources management and the PRA, which was held in February 2005 under the auspices of the National Research Council. GAO also drew on its earlier study of CIO review processes (GAO-05-424) and alternative processes that two agencies have used to minimize burden. For this study, GAO reviewed a governmentwide sample of collections, reviewed processes and collections at four agencies that account for a large proportion of burden, and performed case studies of 12 approved collections."
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 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 | 0.193 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".