Bibliographic record
Abstract
A new solution for reforming U.S. domestic intelligence Domestic intelligence in the United States today is undermanned, uncoordinated, technologically challenged, and dominated by an agencythe FBIthat is structurally unsuited to play the central role in national security intelligence. Despite its importance to national security, it is the weakest link in the U.S. intelligence system. In Remaking Domestic Intelligence, Richard A. Posner reveals all the dangerous weaknesses undermining our domestic intelligence in the United States and offers a new solution: a domestic intelligence agency modeled on the concept and basic design of the Canadian Security Intelligence Service. He details why the FBI, because its primary activity is law enforcement, is not the solution to the problem of domestic intelligence and how a new agency, lodged in the Department of Homeland Security, would have no authority to engage in law enforcement and thus avoid the deep tension between criminal investigation and national security intelligence that plagues the FBI. He also shows how a new U.S. domestic intelligence agency might offer additional advantages over our current structure even in terms of civil liberties. Richard A. Posner is a judge on the U.S. Court of Appeals for the Seventh Circuit and a senior lecturer at the University of Chicago Law School.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".