Is the problem with military culture one of bad apples or bad orchards?: war crimes, scandals, and persistent dysfunction
Bibliographic record
Abstract
This article examines the historic and current role of ‘culture’ in Australian Defence Forces’ responses to scandals, war crimes, and illicit behaviours. It makes the case that the ADF has moved from arguing that illicit activities are the product of isolated soldiers, to arguing that illicit activities are the result of ‘rogue’ groups of soldiers. We call this a shift from the ‘bad apples’’ to the ‘bad orchard’ thesis. Drawing on the concepts of camouflage and building a theoretical understanding of military exceptionalism, we argue we argue ‘military culture’ provides covering fire and camouflage for the institution to protect it from public scrutiny and to hide systemic dysfunction. We also engage with our understanding of institutional gaslighting, to argue that strategies to dismiss and legitimize dysfunction serve to gaslight civilians raising concerns about military conduct by rendering their concerns inexpert, illegitimate, unfounded, or hostile.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".