Teaching Intelligence in the United States, the United Kingdom, and Canada
Bibliographic record
Abstract
Abstract Intelligence studies, as taught by specialized departments or institutes and leading to degrees with the word “intelligence” in their titles, is a relatively new phenomenon. Intelligence is considered a profession, while intelligence studies can probably best be described as an emerging discipline that has yet to reach full maturity. Much of the more recent data on teaching intelligence is in the hands of professional associations, government agencies, and nongovernmental organizations dealing with the intelligence profession. Some of the government academic institutions which served as the wellspring for many of the nongovernmental programs that blossomed later are the Department of Defense institutions, the National Defense Intelligence College, and the National Defense University. There are also professional journals and other publications covering intelligence studies courses, as well as nongovernmental professional organizations that students of intelligence can join, such as the National Military Intelligence Association and the International Studies Association. At the international level, intelligence studies courses are offered in countries like the UK, Canada, Australia, South Africa, Israel, and Brazil. The next step is to determine what specifically is being taught, and how, among the growing number of colleges and universities getting into the business of teaching intelligence, especially in the wake of 9/11. A significant is the phenomenal growth of online programs, which allow deployed military and civilian personnel to study intelligence while practicing the theory they are learning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".