Becoming war: Towards a martial empiricism
Bibliographic record
Abstract
Abstract Under the banner of martial empiricism, we advance a distinctive set of theoretical and methodological commitments for the study of war. Previous efforts to wrestle with this most recalcitrant of phenomena have sought to ground research upon primary definitions or foundational ontologies of war. By contrast, we propose to embrace war’s incessant becoming, making its creativity, mutability and polyvalence central to our enquiry. Leaving behind the interminable quest for its essence, we embrace war as mystery. We draw on a tradition of radical empiricism to devise a conceptual and contextual mode of enquiry that can follow the processes and operations of war wherever they lead us. Moving beyond the instrumental appropriations of strategic thought and the normative strictures typical of critical approaches, martial empiricism calls for an unbounded investigation into the emergent and generative character of war. Framing the accompanying special issue, we outline three domains around which to orient future research: mobilization, design and encounter. Martial empiricism is no idle exercise in philosophical speculation. It holds the promise of a research agenda apposite to the task of fully contending with the momentous possibilities and dangers of war in our time.
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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 | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.095 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".