Operational defense sourcing: organizing military logistics in Afghanistan
Bibliographic record
Abstract
Purpose This paper aims to present a study on the organization of military logistics under “hot” conditions in an expeditionary crisis response operation. The authors' main research question is: in what way is armed forces logistics sourcing organized in the International Security Assistance Force (ISAF) in Afghanistan? Design/methodology/approach To answer their research question, the authors conducted a case study including field research at military sites in Afghanistan. The case study is focused on military organizations that operate in a hostile and ambiguous environment. The authors compare sourcing of three categories of support services, i.e. facilities management, maintenance & logistics and security. Findings The authors' results include a systematic overview of the organization of command, logistic and accounting (sourcing) in the ISAF mission, involving multinational military partners and contractors. Second, the authors show how Canada, NATO, The Netherlands, the United Kingdom and the USA sourced the three categories of services mentioned in terms of sourcing profiles. Focusing on contracting, the authors outline which strategies NATO and the countries mentioned used in practice. And finally, differences and similarities are highlighted in the area of funding and accounting. Research limitations/implications While the authors' study provides insight in the use of sourcing profiles identified in this paper, more research is necessary to identify criteria for explaining sourcing decisions of armed forces. Practical implications The paper provides a systematic overview for practitioners and scholars and enhances manageability and policy development relevant for those who prepare, execute, monitor and evaluate missions. Originality/value The authors' paper is one of the first to provide a systematic overview in operational defense sourcing relying on first‐hand field data. This area of study is fragmented and remains mostly closed for non‐military researchers.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".