Critical Military Epistemology: Designing Reflexivity into Military Curricula
Bibliographic record
Abstract
The author recommends the discipline of the Sociology of Knowledge as an educative underpinning to enhance reflexivity in military practice. The essay develops four postmodern propositions for designing reflexivity into military curricula:1. That military epistemology is an outgrowth of an historic socialization process;2. That using Searle’s fact continuum, we can reveal the subjectivity of military knowledge by exposing the objectivation of socially constructed facts;3. That US military scientism is an ideology, hence, a potential social hazard for those who criticize scientism as the underlying logic of practice; and,4. Critical Military Epistemology (CME), based in the other three propositions, is one educative approach which will enhance reflexivity, providing a plurality of underlying logics of practice. The author offers recommendations for a CME approach, advocating a plurality of onto-epistemological assumptions, to include critically studying military history as a history of military sensemaking, exposing practitioners to the assumptions of SoK, reading the seminal work of Donald A. Schön, and applying CME to promote interdisciplinary awareness. The essay concludes that imbedding CME philosophy in curricula designs will enhance tacit knowledge, emancipatory thinking, reframing, and critical reflexivity in the pursuit of novel underlying logics of practice.
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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 | 0.035 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".