Bibliographic record
Abstract
The author supports the type of the back-of-the-envelope calculations of CPI bias that the Commission used so effectively to attract public attention to its report. In the area of quality adjustment, however, he criticizes the Boskin Commission for what he calls “premature extrapolation, ” that is moving too quickly from a limited number of examples to a broad conclusion. He stresses the importance of high-quality data for policy decisions and observes that a better allocation of existing resources can improve economic statistics, suggesting that the creation of a unified statistical agency in the United States, like Statistics Canada, would streamline data collection and analysis. In terms of the issue of Social Security solvency, the author argues that use of the CPI to adjust social security benefits downward is not a preferred option. He concludes that the Commission should have advised Congress that it did not have an adequate scientific basis to recommend a specific quantitative adjustment to the CPI index used to adjust federal programs.
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 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.026 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".