Intelligence Collection Disciplines—A Systematic Review
Why this work is in the frame
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Bibliographic record
Abstract
Intelligence collection is an integral part of the intelligence cycle. In fact, some authors declare that it is at the heart of the intelligence discipline. Intelligence collection is typically done by a variety of intelligence collection disciplines and is as old as the Bible. In the past, intelligence collection consisted mainly of human intelligence (HUMINT). However, as technologies evolved, so too did collection methods, and the number of collection disciplines, therefore, increased substantially. Some of these intelligence collection disciplines also underwent some significant modifications because of these technological advances. An example of this is Image Intelligence (IMINT) which was previously seen as a collection discipline on its own. IMINT is nowadays considered a subdiscipline under Geospatial Intelligence (GEOINT)—the addition of geographical information systems (GIS) in the 1980s is one of the reasons for this change. These and many other changes resulted in many authors not agreeing on the main disciplines (and subdisciplines) in the intelligence collection domain. Furthermore, different organizations may only perform certain intelligence collection tasks and therefore only consider a certain spectrum of the intelligence collection domain. In 2021, the South African National Defence Force started a new degree programme in Defence Intelligence Studies under the auspices of the Faculty of Military Science, Stellenbosch University. It was, therefore, necessary to first establish what is globally considered the main intelligence collection disciplines and subdisciplines and secondly, which of these must be included when presenting intelligence collection as part of the degree programme in South Africa. The research entailed a two-phased approach, the first part entailed the PRISMA model to find relevant material that was analyzed with ATLAS.ti software during the second phase. The research is interesting since it suggests an expansion of the traditional list of intelligence collection disciplines by adding newer intelligence collection disciplines such as Social Media Intelligence (SOCMINT) and Cyber Intelligence (CYBINT). These additions can also be applied to other educational institutions offering intelligence studies elsewhere in the world.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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