Whole of (coalition) government: Comparing Swedish and German experiences in Afghanistan
Bibliographic record
Abstract
Abstract Whole-of-government (WOG) approaches have emerged as a blueprint for contemporary peace and state-building operations. Countries contributing civilian and military personnel to multinational interventions are persistently urged to improve coherence and enhance coordination between the ministries that form part of the national contingent. Despite a heated debate about what WOG should look like and how to achieve it, the causal mechanisms of WOG variance remains under-theorised. Based on 47 in-depth, semi-structured interviews, this study compares Swedish and German WOG approaches in the context of the International Security Assistance Force (ISAF). I argue that coalition bargaining drove the fluctuation in the Swedish and German WOG models. Strategic culture was an antecedent condition. In both cases, COIN and the war on terror clashed with foundational elements of the Swedish and German strategic cultures, paving the way for a non-debate on WOG on the political arena. Finally, bureaucratic politics was an intervening condition that obstructed or enabled coherence, depending on the ambition of the incumbent coalition government to progress WOG. Overall, the results suggest that coalitions face limitations in implementing a WOG framework when the nature of the military engagement is highly disputed in national parliaments.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".