Highlights of a gao forum: Federal acquisition challenges and opportunities in the 21st century
Bibliographic record
Abstract
Acquisition of products and services from contractors consumes about a quarter of discretionary spending governmentwide and is a key function in many federal agencies. In fiscal year 2005 alone, federal government contracting involved over $388 billion. The work of the government is increasingly being performed by contractors, including in emergency and large-scale logistics operations such as hurricane response and recovery and the war in Iraq. Many agencies rely extensively on contractors to carry out their basic missions. The magnitude of the government's spending and dependence on contractors make it imperative that this function be performed as efficiently and effectively as possible. Yet, acquisition issues are heavily represented on GAO's list of government highrisk areas. In the 21st century, the government needs to reexamine and evaluate its strategic and tactical approaches to acquisition. To identify and discuss the key issues confronting the federal acquisition community, the Comptroller General hosted a forum in July 2006 that brought together acquisition experts from inside and outside the government. Participants shared their insights on challenges and opportunities for improving federal acquisition in an environment of increasing reliance on contractors and severe fiscal constraint.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.028 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".