Bibliographic record
Abstract
Abstract : The federal government workforce is now the smallest it has been in more than 30 years, going all the way back to the Kennedy Administration. (2) The cuts were long overdue. People had long since grown tired of new government programs initiated each year, with none ever ending. They were tired of stories about senseless sounding government jobs, like the Official Tea-Taster, tired of larger and larger bureaucracies in Washington interfering more and more with their lives. For years, presidential candidates have been promising to make government smaller. But until Bill Clinton, none delivered. The workforce cuts are saving lots of money. For fiscal year 1996, the average government worker costs more than $44,000 a year, not including office space and supplies. (3) Cutting a quarter million jobs, therefore, can save well over $10 billion annually. But that's not the half of it. The savings from all the common sense reforms we have put in place total $ 118 billion.* Put that together with the benefits of our healthy economy, and you'll see that the Clinton-Gore Administration has come up with another one for the record books: four straight years of deficit cuts, for a stupendous total reduction of $476 billion.(4) Even though big cuts in government were long overdue, and even though they are a crucial step in getting the country out of the red, there is a right way and a wrong way to cut government. The right way is to show some consideration for the workers.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".