Bibliographic record
Abstract
United States (U.S.) civilian and military intelligence services increasingly have engaged with local intelligence services, either in an advisory role or direct coordination or liaison. In many cases, the intelligence officers have tended to try to remake the local intelligence services in the image of U.S. intelligence structures and procedures, with these efforts rather futile in most cases. One factor that has led to considerable frustration and potential failure has been a lack of understanding of the culture of local intelligence systems. Understanding both the subtleties of an area’s social norms and mores, and the bureaucratic and historical cultures of other intelligence services remain critical factors in long-term success. Using case studies of environments in which established intelligence services have worked with emergent intelligence agencies, this paper examines the requirements for incorporating both larger cultural approaches and detailed knowledge of other intelligence bureaucracies.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".