Bibliographic record
Abstract
The author provides a BLS response to the Boskin Commission from the perspective of ten years following the release of the report. He documents the research on price indexes done at the BLS in the first half of the 1990s that pointed to upward CPI bias, and discusses how these results eventually lead to the appointment of the Boskin Commission in 1995. He also provides a detailed discussion of the methodological changes to the CPI made by the BLS between 1996 and 2002 in three areas corresponding to the categories of bias identified by the Commission: upper and lower level substitution bias, quality change and new products, and outlet bias. These changes included the introduction of a chained CPI (CCPI-U), the introduction of more hedonic models and the recognition of the need to use a product and outlet sample that was as representative as possible of current consumer spending patterns. He concludes that the Boskin Commission, by forcing the BLS to scrutinize the strengths and limitations of its CPI procedures and by highlighting and publicizing the budgetary impacts of the CPI, paved the way for various CPI improvements.
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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.035 | 0.025 |
| Insufficient payload (model declined to judge) | 0.033 | 0.036 |
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".