Preventing Surprise Attacks: Intelligence Reform in the Wake of 9/11; Remaking Domestic Intelligence; and Uncertain Shield: The U.S. Intelligence System in the Throes of Reform, by Richard A. Posner
Bibliographic record
Abstract
Judge Richard Posner responded to the issues of post 9/11 domestic United States intelligence system reform through an article published in the New York Times Review of Books, which grew into a book, augmented by a monograph, and updated by another book. As might be expected of Posner, these short works are lucid, provocative, and informative. The New York Times article, "The 9/11 Report: A Dissent,'" is a review of the recommendations of the 9/11 Commission. Preventing Surprise Attacks' elaborates the article's arguments, particularly in light of the passage of the Intelligence Reform and Terrorism Prevention Act of 2004 (which adopted the Commission's main recommendations), and engages relevant historical and organizational theory literature. The monograph Remaking Domestic Intelligence, focuses on the role of the FBI in domestic national security intelligence. Uncertain Shield updates Preventing Surprise Attacks by addressing the implementation of the IRTP Act and the report of the Commission on the Intelligence Capabilities of the United States Regarding Weapons of Mass Destruction. Certainly these works are valuable for students of American intelligence organization and policy. Posner's treatment of four themes, though, should interest a broader readership. These themes concern (1) the limitations on the capabilities of our intelligence organizations to prevent surprise attacks; (2) the inappropriateness of centralizing the organization of domestic intelligence; (3) the relationship of law enforcement and intelligence gathering; and (4) our attitudes and expectations respecting our safety.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".