Bibliographic record
Abstract
One option for addressing the bias that may result from survey nonresponse is to make greater use of the administrative records that federal and state agencies compile. Such records have been used to assess response bias but less often to correct for such error. Direct substitution of administrative records for survey data, as is done for income data in Canada, provides a means of compensating for survey nonresponse; but the limitations of such data must be recognized. Administrative records may not cover the entire population of interest, may utilize a different unit of observation, may have wide variation in data quality across items or by agency, and may have timeliness issues. In using administrative records, researchers cede control over the content of individual variables, which may differ from survey concepts and be subject to change. Furthermore, privacy protections embodied in law restrict the use of many types of administrative records.
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 | 0.439 | 0.808 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".