Strategic Narratives, Public Opinion, and War: Winning Domestic Support for the Afghan War
Bibliographic record
Abstract
Preface by Jaap de Hoop Scheffer 1. Introduction, Beatrice De Graaf, George Dimitriu and Jens Ringsmose Part I: Theoretical Debates 2. The Possibilities and Limits of Strategic Narratives, Lawrence Freedman 3. Searching for El Dorado: The Legendary Golden Narrative of the Afghanistan War, David Betz 4. Great Power Politics & Strategic Narratives of War, Alister Miskimmon, Ben O'Loughlin and Laura Roselle Part II: Country Perspectives 5. The War in Afghanistan: Australia's Strategic Narratives, William Maley 6. Elite Consensus and Ineffective Strategic Narratives: The Domestic Politics Behind Canada's Commitment to Afghanistan, Justin Massie 7. Czech Strategic Narrative on Afghanistan: Ideological Reactiveness and Domestic Political Contestation, Nik Hynek 8. For our own security and for the sake of the Afghans : How the Danish public was persuaded to support an unprecedented costly military endeavour in Afghanistan, Peter Jakobsen & Jens Ringsmose 9. French Strategic Narratives, Public Opinion and the War in Afghanistan. 2001-2012, Ronald Hatto 10. War-like Circumstances - Germany's Unforeseen Combat Mission in Afghanistan and Its Strategic Narratives, Robin Schroeder and Martin Zapfe 11. Hungary in Afghanistan: A default narrative for a Particularly Prudent Public, Peter Marton and Peter Wagner 12. The winter of our consent? Framing Italy's peace in Afghanistan, Fabrizio Coticchia and Carolina De Simone 13. Fighting versus Reconstructing. Framing the Dutch mission in Afghanistan, Beatrice De Graaf and George Dimitriu 14. Poland's Strategic Narrative on Afghanistan: Getting the Best of Both Worlds, Gorka Winter 15. A Catch-All Strategic Narrative: Target Audiences and Swedish Troop Contribution to ISAF in Afghanistan, Erik Noreen & Jan Angstrom 16. Communicating Afghanistan: Strategic Narratives and the Case of UK Public Opinion, Rikke Bjerg Jensen 17. The Longest War Story: Elite Rhetoric, News Coverage, and the War in Afghanistan (USA), Tim Groeling and Matthew A. Baum 18. Conclusion, Beatrice De Graaf, George Dimitriu and Jens Ringsmose
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".