Bibliographic record
Abstract
This article examines the way in which we organise and combine our efforts during military operations abroad. We seek to illustrate where the current organisations involved would tend to work separately, thus enhancing the chance for missed opportunities, wrong assessment of situations or counter-productive action. To achieve flexibility there has been a great deal of emphasis on the network perspective to organisation, causing concepts such as network enabled capability and network centric warfare to become common good. Based on previous experience in the field, we here propose an additional element that will better allow the various disciplines to work together in a concerted manner providing a good base for human understanding of the situation and effects caused by previous decisions. The main focus of this approach is to influence attitudes and induce a desired behavioural context in the area of operations (AO). These ideas sprouted in Afghanistan during the installation of a fusion cell in 2006 which combined people from various disciplines to assess incoming information; impact of recent events; and impact of our own decisions and actions. Current operations and security environment are increasingly complex and require an organisational structure that is flexible and synergised, creating the necessary pre-conditions for a well conceived Counter-Insurgency (COIN1) approach. The operational environment has to be viewed in a behavioural context. The last decades we have seen situations in which military involvement was not limited to achieving military victory. Rather, it was one of the instruments to influence behaviour. Using this behavioural approach, fusion cell members assess all actors as complex, adaptive, interactive systems-of-systems in a wider context. These actors not only include the local population, leaders and media but also the public and policymakers of troop contributing and other countries of influence. To put these actors in their proper context political, military, cultural, and economical aspects of the environment are taken into account. In this article we highlight the added value of the fusion approach in Afghanistan and make some recommendations for structurally implementing this approach in future COIN operations. http://www.smallwarsjournal.com/documents/28articles.pdf http://usinfo.state.gov/journals/itps/0507/ijpe/kilcullen.htm
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".