Time to move out of the shadows? Special operations forces and accountability
Bibliographic record
Abstract
On his retirement as head of the United States ('US') Joint Special Operations Command, Admiral William McRaven argued that the US was in 'the golden age of Special Operations'. 1 Indeed, according to one 2010 estimate special operations forces ('SOF') from the US were present in 75 countries 2 and by 2013 this had risen to 134. 3 In addition, SOF from Australia, Canada and the United Kingdom ('UK') have been operating in a number of jurisdictions.There are four reasons why SOF have become so prominent in contemporary counterterrorism and counter-insurgency operations.The first is tactical.In the current context SOF have been better able to perform functions that large numbers of troops operating conventionally have not.These include reconnaissance, forward air control, hostage rescue, training and mentoring local forces and, perhaps most controversially, targeted killing.These types of functions have become core to the latest phase of the counter-terrorism operations which began after the 2001 terrorist attacks on the US.In this area SOF operations can be either 'white' (openly acknowledged combat, kill or capture missions, and/or the training and mentoring of local forces) or 'black' (covert or clandestine kill or capture missions and/or assistance to local forces).4 The second reason is strategic.SOF may function, as they arguably are at the moment, as a method of maintaining counter-insurgency and counter-terrorism operations 'under the radar' -reducing the publicity and 'mission creep' that accompanies conventional operations.In
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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 | 0.027 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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".