Terra Incognita: Mapping American Intelligence Education Curriculum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For more than two decades, degree-granting intelligence programs have popped up around the U.S., representing the largest and perhaps most enduring investment in American intelligence education. Scholars have addressed issues in American intelligence education, but to date, there has been no focused study that has mapped and analyzed these programs. This article addresses this gap by answering the questions: What are the American intelligence programs and what content is being taught? We answered this question by systematically identifying all 17 American intelligence education programs (1992-2012). The picture that emerges is one of delayed, but rapid growth: most programs were founded after 2005. After collecting and analyzing hundreds of course descriptions using a widely-accepted qualitative data analysis method called constant comparison, we mapped the curricular structure of the intelligence programs in aggregate. The contribution of this research is to increase understanding of the structure of American intelligence curriculum for current and future intelligence educators as well as employers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it