Bibliographic record
Abstract
This testimony addresses an approach to manage the risk from terrorism directed at Americans in our homeland. With the initiation of military operations against terrorist targets in Afghanistan, senior government officials indicated the need to be prepared for the potential of another attack on our homeland. A body of work in the area of combating terrorism has been undertaken in which various facets of federal efforts to address this challenge have been evaluated. From this work, three essential elements in an effective risk management approach to prepare better against acts of terrorism have been identified. The three key elements that the federal government as well as state and local governments and private entities should adopt to enhance their timely preparedness against potential threats are: (1) a threat assessment; (2) a vulnerability assessment; and (3) a criticality assessment. Threat assessment are important decision support tools that can assist organizations in security-program planning and key efforts. A threat assessment identifies and evaluates threats based on various factors, including capability and intentions as well as the potential lethality of an attack. A vulnerability assessment is a process that identifies weaknesses that may be exploited by terrorists and suggests options to eliminate or mitigate those weaknesses. A criticality assessment is a process designed to systematically identify and evaluate an organization's assets based on the importance of its mission or function, the group of people at risk, or the significance of a structure. Criticality assessment are important because they provide a basis for prioritizing which assets and structures require higher or special protection from an attack.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".