Bibliographic record
Abstract
ABSTRACT For many years now, U.S. defense acquisition and force structure decisions have been based on the premise that the U.S. can and will maintain a commanding technological advantage over potential adversaries. The widespread access to a wide variety of modern top of the line technologies made possible by the globalization of technology research and industrial bases and vastly improved communications has raised concern as to the validity of this premise. This paper discusses the importance of maintaining the ability within U.S. defense and industrial infrastructures to continue to lead the way in developing and integrating breakthrough technologies to maintain the U.S.‘s technological advantage and the role of naval engineers in fostering and managing innovation. It discusses some of the significant obstacles and impacts to the processes of innovation imposed by the inertia within the U.S.'s well‐developed defense and industrial infrastructures and today's fiscally constrained defense environment The need for stable properly prioritized and managed defense research and development resources independent of major platform acquisition programs in order to ensure the U.S.‘s ability to adjust and adapt to strategic uncertainty is identified. Differences between modernization approaches based on incremental, evolutionary change to existing systems and “disruptive” technology, which facilitates the transition from one established path of technology evolution to another, enabling revolutionary change, are also discussed.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".