The Spy Power, Technological Innovations, and the Human Dimensions of Intelligence: Recent Presidential Abuse of America’s Secret Agencies
Bibliographic record
Abstract
The purpose of national security intelligence is to provide policy officials with an advantage in the making of effective policy, based on the collection and analysis of accurate information from around the world that can help to illuminate a decision. Foreknowledge is invaluable in the service of a nation’s security; and, in the gathering of useful information, technological innovations in the world of intelligence can result in a stronger shield to protect citizens against the many dangers that lurk across the continents in this uncertain and hostile world. Despite all the marvels of modern espionage tradecraft, the governments that rely on them must still deal with the human side of intelligence activities. Unfortunately, arrogance, shortsightedness, laziness, frenetic schedules, and the corrosive influences of power (among other flaws) often lead policy officials to ignore or warp the advantages they could accrue from advanced intelligence spycraft, if they would only use these sources and methods properly. This article examines some of the problems that imperfect human behavior has created for intelligence in the United States at the highest levels of government over the past two decades.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".