Narrative approach to the art of war and military studies - Narratology as military science research paradigm
Bibliographic record
Abstract
Abstract The purpose of this article is to initiate discussion into the role narratives could play in military studies. Narratology is an old and well-established research paradigm that first emerged as part of the linguistic turn. Yet its potential has not been depleted. It is the study of narratives or stories. There are plenty of topics not yet approached from this perspective especially in the field of military studies. The military academia needs to broaden its scope of research and allow for alternative orientations and theories to be used to address traditional dilemmas, create new research paradigms and enrich the variety of analysis. Critical security studies approach shared topics with military studies by embracing the aesthetic turn that differentiates between the representation and the represented. The argument in this article is that to produce comprehensive information on its research topics military studies would benefit from embracing them as people experience them and not focus on their ontology. The article does not offer a methodological toolbox to the reader but rather an introduction to some classics of narratology and offers a few insights how this type of approach could be used in military history, strategy, operational art or even leadership studies.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".