Bibliographic record
Abstract
A quarter-century ago, Wilhelm Agrell reflected on the impact of technical collection – which initially consisted of overhead photographic reconnaissance and communications intelligence (COMINT) – on national intelligence. On the positive side, it made it possible for the most advanced countries to get “an almost complete picture of the strength, deployment, and activity of foreign military forces.” The negative, for Agrell, was an overemphasis on what could be counted. Intelligence became “concentrated on evaluation and comparison of military strength based exclusively on numerical factors.” Yet, since the fall of communism and since 11 September 2001, research has not really asked similar questions about the impact of technical collection. To be sure, the highly classified nature of some collection makes it difficult for outsiders to judge the effects on policy outcomes. It is possible, however, to come to the kind of judgments Agrell rendered about the impact of technical collection on the practice of intelligence. So far, however, more serious research has not moved much beyond the post–September 11 conventional wisdom that the change in intelligence's target renders technical collection less effective. Terrorists, it is thought, are small and fleet – networked enough not to depend on large fixed facilities that can be monitored from space and nimble enough to shift to forms of communication, such as couriers, that cannot be intercepted by satellite systems. This chapter assesses research on technical collection, looking across the various “INTs” and, in particular, probing that conventional wisdom.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.044 |
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".