The Fight Against Terrorism—the need for local police units in the United States’ intelligence community
Bibliographic record
Abstract
Post-9/11 saw an undeniable change in the threat landscape, necessitating a differing understanding of how states conduct security. Due to these evolving security concerns, it is important that states foster an inclusive and cooperative intelligence community. The United States’ lack of ability to create such a community, is potentially one of their biggest counterterrorism downfalls. This paper seeks to showcase the cracks in the American intelligence system, and argue that not only does there need to be improved cross-agency communication, but that local police bureaus must be included in the counterterrorism fight. Local police offer unique and valuable skills, which could help to patch several of the holes in current intelligence collection efforts. As the threat environment is continually changing, it is necessary for the United States to put all hands in the proverbial pot for intelligence collection and counterterrorism efforts, in order to adequately speak to the emerging risks to national (and international) security
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 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".