Privatizing Military Production
Bibliographic record
Abstract
The end of the Cold War and subsequent reduction in the size of the military raised many questions about how the Army makes or buys its war materiel. It has a large industrial base, parts of which it owns and operates solely and parts of which are run by civilian contractors. Examples include ammunition plants and arsenals that make heavy ordnance such as gun tubes. The base is large compared with current or anticipated needs and thus underused. Furthermore, much of the equipment is aging and inefficient. Finally, industrial production falls outside the Army's inherently governmental function. Most Western nations with modern armies rely entirely on the private sector to meet their needs for military equipment and ammunition. Indeed, two-thirds of the United States Army's ammunition dollars already go to completely commercial plants. Thus, the question arises: Should privatization play a larger role in the Army's procurement processes? Research carried out in two of the RAND Corporation's federally funded research and development centers, RAND Arroyo Center and RAND National Defense Research Institute, investigated this issue, and the results of the research appear in two publications: "Rethinking Governance of the Army's Arsenals and Ammunition Plants" and "Lessons from the North: Canada's Privatization of Military Ammunition Production." The key findings were as follows: (1) privatizing Army ammunition plants and turning the arsenals into a Federal Government corporation could save the Army money, foster innovation and efficiency, and enable senior leaders to focus on their priority function; (2) potential cost savings range from $525 million to $1 billion in the short term, and from $900 million to $3 billion in the long term; (3) risk associated with privatization and creating a Federal Government corporation is low; and (4) the Canadian experience in privatizing ammunition plants is relevant and support the argument for privatizing U.S. plants.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".