Plays Well With Others: Enhancing DoD's Role in Protecting the National Information Infrastructure
Bibliographic record
Abstract
The terrorist attacks on the twin trade towers and the Pentagon kindled an immediate, renewed focus on homeland defense, Since then, efforts to combat physical terrorist threats have rightly taken center stage, However, the need to protect our national information infrastructure (NII) from an increasing array of cyber threats is equally urgent, This paper will argue that characteristics of the NII drive DoD to a more active role in its defense, it will then discuss NII protection efforts to date, shortfalls in those efforts, and Canada's emerging NII protection structure as a potential model for the US to adopt, Finally it will argue that DoD should have an expanded and better-defined role in NII defense - not as a playground bully that dominates everything, but as a full-fledged team player in areas where it can best apply its expertise. Virtually everyone agrees that the NII is increasingly important to the operation of all our critical national infrastructures, However, expanded NII use has also opened up a new set of cyber vulnerabilities to both the NII itself and the many users who depend on it, Moreover, the ever-expanding NII presents a challenging set of issues to its defenders, The cyberworld blurs the traditional distinctions among different user communities who all now use the common NII, its compression of time and space blurs the ability to distinguish between crime and acts of war, and compounds the task of determining the source of attack. As a result, lines of responsibility blurred among the law enforcement, military, intelligence, and owner-operator communities. These areas of convergence put a premium on a fully cooperative approach to NII protection.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".