Bureau of Prisons: Methods for Cost Estimation Largely Reflect Best Practices, but Quantifying Risks Would Enhance Decision Making
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "The Department of Justice's (DOJ) Federal Bureau of Prisons (BOP) is responsible for the custody and care of about 209,000 federal inmates--a population which has grown by 44 percent over the last decade. In fiscal years 2008 and 2009, the President requested additional funding for BOP because costs for key operations were at risk of exceeding appropriated funding levels. Government Accountability Office (GAO) was congressionally directed to examine (1) how BOP estimates costs when developing its annual budget request to DOJ; (2) the extent to which BOP's methods for estimating costs follow established best practices; and (3) the extent to which BOP's costs for key operations exceeded requested funding levels identified in the President's budget in recent years, and how this has affected BOP's ability to manage its growing inmate population. In conducting our work, GAO analyzed BOP budget documents, interviewed BOP and DOJ officials, and compared BOP's cost estimation documentation to criteria in GAO's Cost Estimating and Assessment Guide."
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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 | 0.023 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".