Domestic Intelligence Reform: Assessing an Independent Domestic Intelligence Agency for the United States
Bibliographic record
Abstract
The purpose of this thesis is to demonstrate how the U.S. would benefit from the creation of an entirely new domestic intelligence agency. To examine this view, this thesis will detail the challenges and limitations faced by existing domestic intelligence efforts (namely the FBI) and assess how the creation of a new agency could serve to remedy many of the existing problems. The thesis evaluates the potential pros and cons of an "agency within an agency” solution (such as the National Security Branch within the FBI) versus the creation of an entirely new domestic intelligence organization. Case studies of similar agencies in other countries (including MI5 in the UK, ASIO in Australia and the Canadian Security Intelligence Service) are evaluated as possible models for the establishment of a successful agency and a means to evaluate potential effectiveness. The thesis addresses additional issues such as American public perceptions of domestic intelligence and balancing concerns for civil liberties with security concerns. The research suggests that the establishment of a new domestic intelligence agency would likely prove beneficial to domestic intelligence efforts, but unless issues with interagency cooperation and bureaucracy are addressed, a new agency may fall victim to the same problems plaguing the current system. Because of the nature of this topic, this thesis will utilize open sources, reports and literature that is publicly available.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".