Bibliographic record
Abstract
This article examines the potential role of private security companies as part of a global special forces network.It reveals three factors that may influence the utility of such companies: (1) the industry's largely defensive focus; (2) the implications of serving a humanitarian and development clientele; and (3) the challenges of retired special forces personnel moving to the private sector.W estern states frequently use the word "network" to describe contemporary military dynamics.Not only are special forces beneficiaries of this reference, they are often proponents for it.1 These forces are ideally suited for networks given their "specialness" and flexibility at the tactical, operational, and strategic levels of war.They have a relatively small footprint, whether in the context of budgets, "boots on the ground," or with respect to much larger and more expensive conventional forces.While these factors are often beneficial, national special forces organizations recognize their quantitative and qualitative shortcomings, especially as they increasingly become a "force of choice."Thus, there is a perceived need to develop a network of like-minded actors.The US Special Operations Command (USSOCOM) has led the way in response to these pressures and, relatedly, to the 2012 Defense Strategic Guidance.For instance, the objective of 2012 International Special Operations Forces Conference was to solidify USSOCOM's prominence and allow others to "gain a better understanding on how to become active members of that network." 2 Similarly, in 2013, the Joint Special Operations University (JSOU), alongside experts and practitioners from other countries, held a conference on "The Role of the Global SOF Network in a Resource Constrained Environment." 3 While these ventures are, in part, about international interoperability, they are also about reaching out and understanding other, non-national, players such as private security companies.Indeed, these firms participated in the JSOU endeavor.Conceiving them as part of a 1 To facilitate readability, the term "special forces" is used here instead of "special operations forces" or "SOF," and does not refer to a specific country's command or organization, unless indicated.The views expressed in this article are those of the author alone and do not necessarily reflect those of the Canadian Department of National Defence or the government of Canada.2 Nigel Chamberlain, "Networks of Special Forces Worldwide," NATO Watch, June 18, 2012, http://www.natowatch.org/node/728.3 The irony is that some of these developing ties between national special forces may be bureaucratic and rule-based rather than based on relationships, thus potentially negating network flexibility.The author wishes to thank Dr. Jessica Glicken Turnley for raising this point.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 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".