Transparency in Statistical Information for Federal Statistical Agencies
Bibliographic record
Abstract
The National Center for Science and Engineering Statistics tasked the Committee on National Statistics to undertake a consensus panel study to examine the degree of transparency and reproducibility of federal statistics. The principal questions were: what should an agency should do to make available, both internally and externally, archives of the input data sets used to generate sets of official statistics; documentation of the treatments done to the raw data prior to the computation of the final estimates (for treatment of failed edits, nonresponse, etc.); and documentation of what goes into the computation of the final published estimates. Presented at IASSIST Professional Development Webinar..
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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 | 0.436 | 0.730 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.032 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.034 | 0.023 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".